The functional landscape of the human phosphoproteome

A basic phosphoproteomic-DIA workflow integrating precise quantification of phosphosites in systems biology

Phosphorylation is one of the most important post-translational modifications (PTMs) of proteins, governing critical protein functions. Most human proteins have been shown to undergo phosphorylation, and phosphoproteomic studies have been widely applied due to recent advancements in high-resolution mass spectrometry technology. Although the experimental workflow for phosphoproteomics has been well-established, it would be useful to optimize and summarize a detailed, feasible protocol that combines phosphoproteomics and data-independent acquisition (DIA), along with follow-up data analysis procedures due to the recent instrumental and bioinformatic advances in measuring and understanding tens of thousands of site-specific phosphorylation events in a single experiment. Here, we describe an optimized Phos-DIA protocol, from sample preparation to bioinformatic analysis, along with practical considerations and experimental configurations for each step. The protocol is designed to be robust and applicable for both small-scale phosphoproteomic analysis and large-scale quantification of hundreds of samples for studies in systems biology and systems medicine.

Decrypting drug actions and protein modifications by dose- and time-resolved proteomics

Although most cancer drugs modulate the activities of cellular pathways by changing posttranslational modifications (PTMs), little is known regarding the extent and the time- and dose-response characteristics of drug-regulated PTMs. In this work, we introduce a proteomic assay called decryptM that quantifies drug-PTM modulation for thousands of PTMs in cells to shed light on target engagement and drug mechanism of action. Examples range from detecting DNA damage by chemotherapeutics, to identifying drug-specific PTM signatures of kinase inhibitors, to demonstrating that rituximab kills CD20-positive B cells by overactivating B cell receptor signaling. DecryptM profiling of 31 cancer drugs in 13 cell lines demonstrates the broad applicability of the approach. The resulting 1.8 million dose-response curves are provided as an interactive molecular resource in ProteomicsDB.

Integrated analysis of SR-like protein kinases Sky1 and Sky2 links signaling networks with transcriptional regulation in Candida albicans

Fungal infections are a major global health burden where Candida albicans is among the most common fungal pathogen in humans and is a common cause of invasive candidiasis. Fungal phenotypes, such as those related to morphology, proliferation and virulence are mainly driven by gene expression, which is primarily regulated by kinase signaling cascades. Serine-arginine (SR) protein kinases are highly conserved among eukaryotes and are involved in major transcriptional processes in human and S. cerevisiae. Candida albicans harbors two SR protein kinases, while Sky2 is important for metabolic adaptation, Sky1 has similar functions as in S. cerevisiae. To investigate the role of these SR kinases for the regulation of transcriptional responses in C. albicans, we performed RNA sequencing of sky1Δ and sky2Δ and integrated a comprehensive phosphoproteome dataset of these mutants. Using a Systems Biology approach, we study transcriptional regulation in the context of kinase signaling networks. Transcriptomic enrichment analysis indicates that pathways involved in the regulation of gene expression are downregulated and mitochondrial processes are upregulated in sky1Δ. In sky2Δ, primarily metabolic processes are affected, especially for arginine, and we observed that arginine-induced hyphae formation is impaired in sky2Δ. In addition, our analysis identifies several transcription factors as potential drivers of the transcriptional response. Among these, a core set is shared between both kinase knockouts, but it appears to regulate different subsets of target genes. To elucidate these diverse regulatory patterns, we created network modules by integrating the data of site-specific protein phosphorylation and gene expression with kinase-substrate predictions and protein-protein interactions. These integrated signaling modules reveal shared parts but also highlight specific patterns characteristic for each kinase. Interestingly, the modules contain many proteins involved in fungal morphogenesis and stress response. Accordingly, experimental phenotyping shows a higher resistance to Hygromycin B for sky1Δ. Thus, our study demonstrates that a combination of computational approaches with integration of experimental data can offer a new systems biological perspective on the complex network of signaling and transcription. With that, the investigation of the interface between signaling and transcriptional regulation in C. albicans provides a deeper insight into how cellular mechanisms can shape the phenotype.

Recent advances in kinase signaling network profiling by mass spectrometry

Mass spectrometry-based phosphoproteomics is currently the leading methodology for the study of global kinase signaling. The scientific community is continuously releasing technological improvements for sensitive and fast identification of phosphopeptides, and their accurate quantification. To interpret large-scale phosphoproteomics data, numerous bioinformatic resources are available that help understanding kinase network functional role in biological systems upon perturbation. Some of these resources are databases of phosphorylation sites, protein kinases and phosphatases; others are bioinformatic algorithms to infer kinase activity, predict phosphosite functional relevance and visualize kinase signaling networks. In this review, we present the latest experimental and bioinformatic tools to profile protein kinase signaling networks and provide examples of their application in biomedicine.

Principles of phosphoproteomics and applications in cancer research

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

FCGR2A as one novel potential target for poor survival prognosis of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma. Immunoglobulin FcγRIIa receptor (FCGR2A) has been implicated in various cancers, however, its role on ccRCC is not well studied. A total of 151 patients with ccRCC were recruited for the study. Cox proportional hazards regression analysis was performed to calculate the hazard radios of FCGR2A expression and tumor characteristics. Pathological changes associated with ccRCC in tumor tissue sections were analyzed by hematoxylin-eosin staining. Immunohistochemical and immunofluorescence staining were used to detect the protein expression of FCGR2A in the tissue sections. Correlation between the expression of FCGR2A and the overall survival (OS) of ccRCC patients was analyzed by biological process neural network and support vector machine. The expression of FCGR2A was significantly correlated with the TNM of tumor, family history of ccRCC and Fuhrman stage of ccRCC. Patients with high FCGR2A expression in the tumor tissue, had poorer OS than the patients with low and moderate FCGR2A expression. The Receiver operating characteristic curve showed that FCGR2A can be used as a sensitive and specific biomarker for the diagnosis of ccRCC. Western blotting revealed that the FCGR2A was expressed at higher levels in the ccRCC tissues. Biological process neural network and support vector machine fitting showed that the R2 between FCGR2A and survival time of ccRCC patients was 0.8429 and 0.7669, respectively. FCGR2A is highly expressed in ccRCC, higher expression of FCGR2A is associated with poorer OS of ccRCC.

Pan-Cancer landscape of protein activities identifies drivers of signalling dysregulation and patient survival

Genetic alterations in cancer cells trigger oncogenic transformation, a process largely mediated by the dysregulation of kinase and transcription factor (TF) activities. While the mutational profiles of thousands of tumours have been extensively characterised, the measurements of protein activities have been technically limited until recently. We compiled public data of matched genomics and (phospho)proteomics measurements for 1,110 tumours and 77 cell lines that we used to estimate activity changes in 218 kinases and 292 TFs. Co-regulation of kinase and TF activities reflects previously known regulatory relationships and allows us to dissect genetic drivers of signalling changes in cancer. We find that loss-of-function mutations are not often associated with the dysregulation of downstream targets, suggesting frequent compensatory mechanisms. Finally, we identified the activities most differentially regulated in cancer subtypes and showed how these can be linked to differences in patient survival. Our results provide broad insights into the dysregulation of protein activities in cancer and their contribution to disease severity.

Characterization of p38α signaling networks in cancer cells using quantitative proteomics and phosphoproteomics

p38α (encoded by MAPK14) is a protein kinase that regulates cellular responses to almost all types of environmental and intracellular stresses. Upon activation, p38α phosphorylates many substrates both in the cytoplasm and nucleus, allowing this pathway to regulate a wide variety of cellular processes. While the role of p38α in the stress response has been widely investigated, its implication in cell homeostasis is less understood. To investigate the signaling networks regulated by p38α in proliferating cancer cells, we performed quantitative proteomic and phosphoproteomic analyses in breast cancer cells in which this pathway had been either genetically targeted or chemically inhibited. Our study identified with high confidence 35 proteins and 82 phosphoproteins (114 phosphosites) that are modulated by p38α, and highlighted the implication of various protein kinases, including MK2 and mTOR, in the p38α-regulated signaling networks. Moreover, functional analyses revealed an important contribution of p38α to the regulation of cell adhesion, DNA replication and RNA metabolism. Indeed, we provide experimental evidence supporting that p38α facilitates cancer cell adhesion, and showed that this p38α function is likely mediated by the modulation of the adaptor protein ArgBP2. Collectively, our results illustrate the complexity of the p38α regulated signaling networks, provide valuable information on p38α-dependent phosphorylation events in cancer cells, and document a mechanism by which p38α can regulate cell adhesion.

Protein kinase CK2 modulates the activity of Maf-family bZIP transcription factor NRL in rod photoreceptors of mammalian retina

Maf-family basic motif leucine zipper protein NRL specifies rod photoreceptor cell fate during retinal development and, in concert with homeodomain protein CRX and other regulatory factors, controls the expression of most rod-expressed genes including the visual pigment gene Rhodopsin (Rho). Transcriptional regulatory activity of NRL is modulated by post-translational modifications, especially phosphorylation, and mutations at specific phosphosites can lead to retinal degeneration. During our studies to elucidate NRL-mediated transcriptional regulation, we identified protein kinase CK2 in NRL-enriched complexes bound to Rho promoter-enhancer regions and in NRL-enriched high molecular mass fractions from the bovine retina. The presence of CK2 in NRL complexes was confirmed by co-immunoprecipitation from developing and adult mouse retinal extracts. In vitro kinase assay and bioinformatic analysis indicated phosphorylation of NRL at Ser117 residue by CK2. Co-transfection of Csnk2a1 cDNA encoding murine CK2 with human NRL and CRX reduced the bovine Rho promoter-driven luciferase expression in HEK293 cells and mutagenesis of NRL-Ser117 residue to Ala restored the reporter gene activity. In concordance, overexpression of CK2 in the mouse retina in vivo by electroporation resulted in reduction of Rho promoter-driven DsRed reporter expression as well as the transcript level of many phototransduction genes. Thus, our studies demonstrate that CK2 can phosphorylate Ser117 of NRL. Modulation of NRL activity by CK2 suggests intricate interdependence of transcriptional and signaling pathways in maintaining rod homeostasis.

Toward an Integrated Machine Learning Model of a Proteomics Experiment

In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.

Integrated View of Baseline Protein Expression in Human Tissues

The availability of proteomics datasets in the public domain, and in the PRIDE database, in particular, has increased dramatically in recent years. This unprecedented large-scale availability of data provides an opportunity for combined analyses of datasets to get organism-wide protein abundance data in a consistent manner. We have reanalyzed 24 public proteomics datasets from healthy human individuals to assess baseline protein abundance in 31 organs. We defined tissue as a distinct functional or structural region within an organ. Overall, the aggregated dataset contains 67 healthy tissues, corresponding to 3,119 mass spectrometry runs covering 498 samples from 489 individuals. We compared protein abundances between different organs and studied the distribution of proteins across these organs. We also compared the results with data generated in analogous studies. Additionally, we performed gene ontology and pathway-enrichment analyses to identify organ-specific enriched biological processes and pathways. As a key point, we have integrated the protein abundance results into the resource Expression Atlas, where they can be accessed and visualized either individually or together with gene expression data coming from transcriptomics datasets. We believe this is a good mechanism to make proteomics data more accessible for life scientists.

Phosphorylation of Influenza A Virus Matrix Protein 1 at Threonine 108 Controls Its Multimerization State and Functional Association with the STRIPAK Complex

The influenza A virus (IAV)-encoded matrix protein 1 (M1) acts as a master regulator of virus replication and fulfills multiple structural and regulatory functions in different cell compartments. Therefore, the spatiotemporal regulation of M1 is achieved by different mechanisms, including its structural and pH-dependent flexibility, differential association with cellular factors, and posttranslational modifications. Here, we investigated the function of M1 phosphorylation at the evolutionarily conserved threonine 108 (T108) and found that its mutation to a nonphosphorylatable alanine prohibited virus replication. Absent T108, phosphorylation led to strongly increased self-association of M1 at the cell membrane and consequently prohibited its ability to enter the nucleus and to contribute to viral ribonucleoprotein nuclear export. M1 T108 phosphorylation also controls the binding affinity to the cellular STRIPAK (striatin-interacting phosphatases and kinases) complex, which contains different kinases and the phosphatase PP2A to shape phosphorylation-dependent signaling networks. IAV infection led to the redistribution of the STRIPAK scaffolding subunits STRN and STRN3 from the cell membrane to cytosolic and perinuclear clusters, where it colocalized with M1. Inactivation of the STRIPAK complex resulted in compromised M1 polymerization and IAV replication. IMPORTANCE Influenza viruses pose a major threat to human health and cause annual epidemics and occasional pandemics. Many virus-encoded proteins exert various functions in different subcellular compartments, as exemplified by the M1 protein, but the molecular mechanisms endowing the multiplicity of functions remain incompletely understood. Here, we report that phosphorylation of M1 at T108 is essential for virus replication and controls its propensity for self-association and nuclear localization. This phosphorylation also controls binding affinity of the M1 protein to the STRIPAK complex, which contributes to M1 polymerization and virus replication.

Phosphoproteomics reveals rewiring of the insulin signaling network and multi-nodal defects in insulin resistance

The failure of metabolic tissues to appropriately respond to insulin ("insulin resistance") is an early marker in the pathogenesis of type 2 diabetes. Protein phosphorylation is central to the adipocyte insulin response, but how adipocyte signaling networks are dysregulated upon insulin resistance is unknown. Here we employ phosphoproteomics to delineate insulin signal transduction in adipocyte cells and adipose tissue. Across a range of insults causing insulin resistance, we observe a marked rewiring of the insulin signaling network. This includes both attenuated insulin-responsive phosphorylation, and the emergence of phosphorylation uniquely insulin-regulated in insulin resistance. Identifying dysregulated phosphosites common to multiple insults reveals subnetworks containing non-canonical regulators of insulin action, such as MARK2/3, and causal drivers of insulin resistance. The presence of several bona fide GSK3 substrates among these phosphosites led us to establish a pipeline for identifying context-specific kinase substrates, revealing widespread dysregulation of GSK3 signaling. Pharmacological inhibition of GSK3 partially reverses insulin resistance in cells and tissue explants. These data highlight that insulin resistance is a multi-nodal signaling defect that includes dysregulated MARK2/3 and GSK3 activity.

Towards a structurally resolved human protein interaction network

Cellular functions are governed by molecular machines that assemble through protein-protein interactions. Their atomic details are critical to studying their molecular mechanisms. However, fewer than 5% of hundreds of thousands of human protein interactions have been structurally characterized. Here we test the potential and limitations of recent progress in deep-learning methods using AlphaFold2 to predict structures for 65,484 human protein interactions. We show that experiments can orthogonally confirm higher-confidence models. We identify 3,137 high-confidence models, of which 1,371 have no homology to a known structure. We identify interface residues harboring disease mutations, suggesting potential mechanisms for pathogenic variants. Groups of interface phosphorylation sites show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple protein interactions as signaling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies helping to expand our understanding of human cell biology.

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.

Control of protein stability by post-translational modifications

Post-translational modifications (PTMs) can occur on specific amino acids localized within regulatory domains of target proteins, which control a protein's stability. These regions, called degrons, are often controlled by PTMs, which act as signals to expedite protein degradation (PTM-activated degrons) or to forestall degradation and stabilize a protein (PTM-inactivated degrons). We summarize current knowledge of the regulation of protein stability by various PTMs. We aim to display the variety and breadth of known mechanisms of regulation as well as highlight common themes in PTM-regulated degrons to enhance potential for identifying novel drug targets where druggable targets are currently lacking.

The ProteomeXchange consortium at 10 years: 2023 update

Mass spectrometry (MS) is by far the most used experimental approach in high-throughput proteomics. The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) was originally set up to standardize data submission and dissemination of public MS proteomics data. It is now 10 years since the initial data workflow was implemented. In this manuscript, we describe the main developments in PX since the previous update manuscript in Nucleic Acids Research was published in 2020. The six members of the Consortium are PRIDE, PeptideAtlas (including PASSEL), MassIVE, jPOST, iProX and Panorama Public. We report the current data submission statistics, showcasing that the number of datasets submitted to PX resources has continued to increase every year. As of June 2022, more than 34 233 datasets had been submitted to PX resources, and from those, 20 062 (58.6%) just in the last three years. We also report the development of the Universal Spectrum Identifiers and the improvements in capturing the experimental metadata annotations. In parallel, we highlight that data re-use activities of public datasets continue to increase, enabling connections between PX resources and other popular bioinformatics resources, novel research and also new data resources. Finally, we summarise the current state-of-the-art in data management practices for sensitive human (clinical) proteomics data.

EMBL's European Bioinformatics Institute (EMBL-EBI) in 2022

The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the Wellcome Genome Campus in Hinxton, UK, EMBL-EBI is one of six sites of the European Molecular Biology Laboratory (EMBL), Europe's only intergovernmental life sciences organisation. This overview summarises the status of services that EMBL-EBI data resources provide to scientific communities globally. The scale, openness, rich metadata and extensive curation of EMBL-EBI added-value databases makes them particularly well-suited as training sets for deep learning, machine learning and artificial intelligence applications, a selection of which are described here. The data resources at EMBL-EBI can catalyse such developments because they offer sustainable, high-quality data, collected in some cases over decades and made openly availability to any researcher, globally. Our aim is for EMBL-EBI data resources to keep providing the foundations for tools and research insights that transform fields across the life sciences.

An atlas of substrate specificities for the human serine/threonine kinome

Protein phosphorylation is one of the most widespread post-translational modifications in biology1,2. With advances in mass-spectrometry-based phosphoproteomics, 90,000 sites of serine and threonine phosphorylation have so far been identified, and several thousand have been associated with human diseases and biological processes3,4. For the vast majority of phosphorylation events, it is not yet known which of the more than 300 protein serine/threonine (Ser/Thr) kinases encoded in the human genome are responsible3. Here we used synthetic peptide libraries to profile the substrate sequence specificity of 303 Ser/Thr kinases, comprising more than 84% of those predicted to be active in humans. Viewed in its entirety, the substrate specificity of the kinome was substantially more diverse than expected and was driven extensively by negative selectivity. We used our kinome-wide dataset to computationally annotate and identify the kinases capable of phosphorylating every reported phosphorylation site in the human Ser/Thr phosphoproteome. For the small minority of phosphosites for which the putative protein kinases involved have been previously reported, our predictions were in excellent agreement. When this approach was applied to examine the signalling response of tissues and cell lines to hormones, growth factors, targeted inhibitors and environmental or genetic perturbations, it revealed unexpected insights into pathway complexity and compensation. Overall, these studies reveal the intrinsic substrate specificity of the human Ser/Thr kinome, illuminate cellular signalling responses and provide a resource to link phosphorylation events to biological pathways.

Phosphoproteomic analysis identifies differentially expressed phosphorylation sites that affect muscle fiber type in pigs

Skeletal muscle of livestock is composed of both fast- and slow-twitch muscle fibers, which are key factors in their meat quality. However, the role of protein phosphorylation in muscle fiber type is not completely understood. Here, a fast-twitch (biceps femoris, BF) and slow-twitch (soleus, SOL) muscle tissue sample was collected from three male offspring of Duroc and Meishan pigs. We demonstrate that the meat quality of SOL muscle is significantly better than that of BF muscle. We further used phosphoproteomic profiling of BF and SOL muscles to identify differences between these muscle types. A total of 2,327 phosphorylation sites from 770 phosphoproteins were identified. Among these sites, 287 differentially expressed phosphorylation sites (DEPSs) were identified between BF and SOL. GO and KEGG enrichment analysis of proteins containing DEPSs showed that these phosphorylated proteins were enriched in the glycolytic process GO term and the AMPK signaling pathway. A protein-protein interaction (PPI) analysis reveals that these phosphorylated proteins interact with each other to regulate the transformation of muscle fiber type. These analyses reveal that protein phosphorylation modifications are involved in porcine skeletal muscle fiber type transformation. This study provides new insights into the molecular mechanisms by which protein phosphorylation regulates muscle fiber type transformation and meat quality in pigs.

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

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

Proteomic Insights into Cardiac Fibrosis: From Pathophysiological Mechanisms to Therapeutic Opportunities

Cardiac fibrosis is a common pathophysiologic process in nearly all forms of heart disease which refers to excessive deposition of extracellular matrix proteins by cardiac fibroblasts. Activated fibroblasts are the central cellular effectors in cardiac fibrosis, and fibrotic remodelling can cause several cardiac dysfunctions either by reducing the ejection fraction due to a stiffened myocardial matrix, or by impairing electric conductance. Recently, there is a rising focus on the proteomic studies of cardiac fibrosis for pathogenesis elucidation and potential biomarker mining. This paper summarizes the current knowledge of molecular mechanisms underlying cardiac fibrosis, discusses the potential of imaging and circulating biomarkers available to recognize different phenotypes of this lesion, reviews the currently available and potential future therapies that allow individualized management in reversing progressive fibrosis, as well as the recent progress on proteomic studies of cardiac fibrosis. Proteomic approaches using clinical specimens and animal models can provide the ability to track pathological changes and new insights into the mechanisms underlining cardiac fibrosis. Furthermore, spatial and cell-type resolved quantitative proteomic analysis may also serve as a minimally invasive method for diagnosing cardiac fibrosis and allowing for the initiation of prophylactic treatment.

Protein-Peptide Turnover Profiling reveals the order of PTM addition and removal during protein maturation

Post-translational modifications (PTMs) regulate various aspects of protein function, including degradation. Mass spectrometric methods relying on pulsed metabolic labeling are popular to quantify turnover rates on a proteome-wide scale. Such data have traditionally been interpreted in the context of protein proteolytic stability. Here, we combine theoretical kinetic modeling with experimental pulsed stable isotope labeling of amino acids in cell culture (pSILAC) for the study of protein phosphorylation. We demonstrate that metabolic labeling combined with PTM-specific enrichment does not measure effects of PTMs on protein stability. Rather, it reveals the relative order of PTM addition and removal along a protein's lifetime-a fundamentally different metric. This is due to interconversion of the measured proteoform species. Using this framework, we identify temporal phosphorylation sites on cell cycle-specific factors and protein complex assembly intermediates. Our results thus allow tying PTMs to the age of the modified proteins.

Proteomic characterization of post-translational modifications in drug discovery

Protein post-translational modifications (PTMs), which are usually enzymatically catalyzed, are major regulators of protein activity and involved in almost all celluar processes. Dysregulation of PTMs is associated with various types of diseases. Therefore, PTM regulatory enzymes represent as an attractive and important class of targets in drug research and development. Inhibitors against kinases, methyltransferases, deacetyltransferases, ubiquitin ligases have achieved remarkable success in clinical application. Mass spectrometry-based proteomics technologies serve as a powerful approach for system-wide characterization of PTMs, which facilitates the identification of drug targets, elucidation of the mechanisms of action of drugs, and discovery of biomakers in personalized therapy. In this review, we summarize recent advances of proteomics-based studies on PTM targeting drugs and discuss how proteomics strategies facilicate drug target identification, mechanism elucidation, and new therapy development in precision medicine.

Where protein structure and cell diversity meet

Protein-protein interaction networks - interactomes - are charted with the hope to understand how phenotypes emerge and how they are altered in disease states. Early efforts to map interactomes have focused on the assembly of context agnostic, reference networks. However, recent studies have mapped interactomes across different cell lines and tissues, finding highly variable interactomes due to the rewiring of protein-protein interactions in different contexts. Increasing evidence points to significant links between protein structure and interactome diversity seen across cell types and tissues. We discuss how recent findings support the key role of alternative splicing and phosphorylation, two well-established regulators of protein structural and functional diversity, in defining cell type- and tissue-specific interactomes. Moreover, we show that intrinsically disordered protein regions are most favorably equipped to support interactome rewiring by acting as hubs of protein structure and function regulation.

Accessing isotopically labeled proteins containing genetically encoded phosphoserine for NMR with optimized expression conditions

Phosphoserine (pSer) sites are primarily located within disordered protein regions, making it difficult to experimentally ascertain their effects on protein structure and function. Therefore, the production of 15N- (and 13C)-labeled proteins with site-specifically encoded pSer for NMR studies is essential to uncover molecular mechanisms of protein regulation by phosphorylation. While genetic code expansion technologies for the translational installation of pSer in Escherichia coli are well established and offer a powerful strategy to produce site-specifically phosphorylated proteins, methodologies to adapt them to minimal or isotope-enriched media have not been described. This shortcoming exists because pSer genetic code expansion expression hosts require the genomic ΔserB mutation, which increases pSer bioavailability but also imposes serine auxotrophy, preventing growth in minimal media used for isotopic labeling of recombinant proteins. Here, by testing different media supplements, we restored normal BL21(DE3) ΔserB growth in labeling media but subsequently observed an increase of phosphatase activity and mis-incorporation not typically seen in standard rich media. After rounds of optimization and adaption of a high-density culture protocol, we were able to obtain ≥10 mg/L homogenously labeled, phosphorylated superfolder GFP. To demonstrate the utility of this method, we also produced the intrinsically disordered serine/arginine-rich region of the SARS-CoV-2 Nucleocapsid protein labeled with 15N and pSer at the key site S188 and observed the resulting peak shift due to phosphorylation by 2D and 3D heteronuclear single quantum correlation analyses. We propose this cost-effective methodology will pave the way for more routine access to pSer-enriched proteins for 2D and 3D NMR analyses.

SMARCC2 mediates the regulation of DKK1 by the transcription factor EGR1 through chromatin remodeling to reduce the proliferative capacity of glioblastoma

Switch/sucrose-nonfermenting (SWI/SNF) complexes play a key role in chromatin remodeling. Recent studies have found that SMARCC2, as the core subunit of the fundamental module of the complex, plays a key role in its early assembly. In this study, we found a unique function of SMARCC2 in inhibiting the progression of glioblastoma by targeting the DKK1 signaling axis. Low expression of SMARCC2 is found in malignant glioblastoma (GBM) compared with low-grade gliomas. SMARCC2 knockout promoted the proliferation of glioblastoma cells, while its overexpression showed the opposite effect. Mechanistically, SMARCC2 negatively regulates transcription by dynamically regulating the chromatin structure and closing the promoter region of the target gene DKK1, which can be bound by the transcription factor EGR1. DKK1 knockdown significantly reduced the proliferation of glioblastoma cell lines by inhibiting the PI3K-AKT pathway. We also studied the functions of the SWIRM and SANT domains of SMARCC2 and found that the SWIRM domain plays a more important role in the complete chromatin remodeling function of SMARCC2. In addition, in vivo studies confirmed that overexpression of SMARCC2 could significantly inhibit the size of intracranial gliomas in situ in nude mice. Overall, this study shows that SMARCC2, as a tumor suppressor, inhibits the proliferation of glioblastoma by targeting the transcription of the oncogene DKK1 through chromatin remodeling, indicating that SMARCC2 is a potentially attractive therapeutic target in glioblastoma.

Characterization of TGF-β signaling in a human organotypic skin model reveals that loss of TGF-βRII induces invasive tissue growth

Transforming growth factor–β (TGF-β) signaling regulates various aspects of cell growth and differentiation and is often dysregulated in human cancers. We combined genetic engineering of a human organotypic three-dimensional (3D) skin model with global quantitative proteomics and phosphoproteomics to dissect the importance of essential components of the TGF-β signaling pathway, including the ligands TGF-β1, TGF-β2, and TGF-β3, the receptor TGF-βRII, and the intracellular effector SMAD4. Consistent with the antiproliferative effects of TGF-β signaling, the loss of TGF-β1 or SMAD4 promoted cell cycling and delayed epidermal differentiation. The loss of TGF-βRII, which abrogates both SMAD4-dependent and SMAD4-independent downstream signaling, more strongly affected cell proliferation and differentiation than did loss of SMAD4, and it induced invasive growth. TGF-βRII knockout reduced cell-matrix interactions, and the production of matrix proteins increased the production of cancer-associated cell-cell adhesion proteins and proinflammatory mediators and increased mitogen-activated protein kinase (MAPK) signaling. Inhibiting the activation of the ERK and p38 MAPK pathways blocked the development of the invasive phenotype upon the loss of TGF-βRII. This study provides a framework for exploring TGF-β signaling pathways in human epithelial tissue homeostasis and transformation using genetic engineering, 3D tissue models, and high-throughput quantitative proteomics and phosphoproteomics.

Phosphorylation of the smooth muscle master splicing regulator RBPMS regulates its splicing activity

We previously identified RBPMS as a master regulator of alternative splicing in differentiated smooth muscle cells (SMCs). RBPMS is transcriptionally downregulated during SMC dedifferentiation, but we hypothesized that RBPMS protein activity might be acutely downregulated by post-translational modifications. Publicly available phosphoproteomic datasets reveal that Thr113 and Thr118 immediately adjacent to the RRM domain are commonly both phosphorylated. An RBPMS T113/118 phosphomimetic T/E mutant showed decreased splicing regulatory activity both in transfected cells and in a cell-free in vitro assay, while a non-phosphorylatable T/A mutant retained full activity. Loss of splicing activity was associated with a modest reduction in RNA affinity but significantly reduced RNA binding in nuclear extract. A lower degree of oligomerization of the T/E mutant might cause lower avidity of multivalent RNA binding. However, NMR analysis also revealed that the T113/118E peptide acts as an RNA mimic which can loop back and antagonize RNA-binding by the RRM domain. Finally, we identified ERK2 as the most likely kinase responsible for phosphorylation at Thr113 and Thr118. Collectively, our data identify a potential mechanism for rapid modulation of the SMC splicing program in response to external signals during the vascular injury response and atherogenesis.

Evolutionary Signals in Coronaviral Structural Proteins Suggest Possible Complex Mechanisms of Post-Translational Regulation in SARS-CoV-2 Virus

Post-translational regulation of proteins has emerged as a central topic of research in the field of functional proteomics. Post-translational modifications (PTMs) dynamically control the activities of proteins and are involved in a wide range of biological processes. Crosstalk between different types of PTMs represents a key mechanism of regulation and signaling. Due to the current pandemic of the novel and dangerous SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) virus, here we present an in silico analysis of different types of PTMs in structural proteins of coronaviruses. A dataset of PTM sites was studied at three levels: conservation analysis, mutational analysis and crosstalk analysis. We identified two sets of PTMs which could have important functional roles in the regulation of the structural proteins of coronaviruses. Additionally, we found seven interesting signals of potential crosstalk events. These results reveal a higher level of complexity in the mechanisms of post-translational regulation of coronaviral proteins and provide new insights into the adaptation process of the SARS-CoV-2 virus.

Multi-omic profiling reveals the ataxia protein sacsin is required for integrin trafficking and synaptic organization

Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is a childhood-onset cerebellar ataxia caused by mutations in SACS, which encodes the protein sacsin. Cellular ARSACS phenotypes include mitochondrial dysfunction, intermediate filament disorganization, and progressive death of cerebellar Purkinje neurons. It is unclear why the loss of sacsin causes these deficits or why they manifest as cerebellar ataxia. Here, we perform multi-omic profiling in sacsin knockout (KO) cells and identify alterations in microtubule dynamics and mislocalization of focal adhesion (FA) proteins, including multiple integrins. Deficits in FA structure, signaling, and function can be rescued by targeting PTEN, a negative regulator of FA signaling. ARSACS mice possess mislocalization of ITGA1 in Purkinje neurons and synaptic disorganization in the deep cerebellar nucleus (DCN). The sacsin interactome reveals that sacsin regulates interactions between cytoskeletal and synaptic adhesion proteins. Our findings suggest that disrupted trafficking of synaptic adhesion proteins is a causal molecular deficit in ARSACS.

A multi-purpose, regenerable, proteome-scale, human phosphoserine resource for phosphoproteomics

Mass-spectrometry-based phosphoproteomics has become indispensable for understanding cellular signaling in complex biological systems. Despite the central role of protein phosphorylation, the field still lacks inexpensive, regenerable, and diverse phosphopeptides with ground-truth phosphorylation positions. Here, we present Iterative Synthetically Phosphorylated Isomers (iSPI), a proteome-scale library of human-derived phosphoserine-containing phosphopeptides that is inexpensive, regenerable, and diverse, with precisely known positions of phosphorylation. We demonstrate possible uses of iSPI, including use as a phosphopeptide standard, a tool to evaluate and optimize phosphorylation-site localization algorithms, and a benchmark to compare performance across data analysis pipelines. We also present AScorePro, an updated version of the AScore algorithm specifically optimized for phosphorylation-site localization in higher energy fragmentation spectra, and the FLR viewer, a web tool for phosphorylation-site localization, to enable community use of the iSPI resource. iSPI and its associated data constitute a useful, multi-purpose resource for the phosphoproteomics community.

Ensemble learning-based feature selection for phosphorylation site detection

SARS-COV-2 is prevalent all over the world, causing more than six million deaths and seriously affecting human health. At present, there is no specific drug against SARS-COV-2. Protein phosphorylation is an important way to understand the mechanism of SARS -COV-2 infection. It is often expensive and time-consuming to identify phosphorylation sites with specific modified residues through experiments. A method that uses machine learning to make predictions about them is proposed. As all the methods of extracting protein sequence features are knowledge-driven, these features may not be effective for detecting phosphorylation sites without a complete understanding of the mechanism of protein. Moreover, redundant features also have a great impact on the fitting degree of the model. To solve these problems, we propose a feature selection method based on ensemble learning, which firstly extracts protein sequence features based on knowledge, then quantifies the importance score of each feature based on data, and finally uses the subset of important features as the final features to predict phosphorylation sites.

Phosphoproteomics of three exercise modalities identifies canonical signaling and C18ORF25 as an AMPK substrate regulating skeletal muscle function

Exercise induces signaling networks to improve muscle function and confer health benefits. To identify divergent and common signaling networks during and after different exercise modalities, we performed a phosphoproteomic analysis of human skeletal muscle from a cross-over intervention of endurance, sprint, and resistance exercise. This identified 5,486 phosphosites regulated during or after at least one type of exercise modality and only 420 core phosphosites common to all exercise. One of these core phosphosites was S67 on the uncharacterized protein C18ORF25, which we validated as an AMPK substrate. Mice lacking C18ORF25 have reduced skeletal muscle fiber size, exercise capacity, and muscle contractile function, and this was associated with reduced phosphorylation of contractile and Ca2+ handling proteins. Expression of C18ORF25 S66/67D phospho-mimetic reversed the decreased muscle force production. This work defines the divergent and canonical exercise phosphoproteome across different modalities and identifies C18ORF25 as a regulator of exercise signaling and muscle function.

Emerging insights into serine/threonine-specific phosphoprotein phosphatase function and selectivity

Protein phosphorylation on serine and threonine residues is a widely distributed post-translational modification on proteins that acts to regulate their function. Phosphoprotein phosphatases (PPPs) contribute significantly to a plethora of cellular functions through the accurate dephosphorylation of phosphorylated residues. Most PPPs accomplish their purpose through the formation of complex holoenzymes composed of a catalytic subunit with various regulatory subunits. PPP holoenzymes then bind and dephosphorylate substrates in a highly specific manner. Despite the high prevalence of PPPs and their important role for cellular function, their mechanisms of action in the cell are still not well understood. Nevertheless, substantial experimental advancements in (phospho-)proteomics, structural and computational biology have contributed significantly to a better understanding of PPP biology in recent years. This Review focuses on recent approaches and provides an overview of substantial new insights into the complex mechanism of PPP holoenzyme regulation and substrate selectivity.

qPTM: an updated database for PTM dynamics in human, mouse, rat and yeast

Post-translational modifications (PTMs) are critical molecular mechanisms that regulate protein functions temporally and spatially in various organisms. Since most PTMs are dynamically regulated, quantifying PTM events under different states is crucial for understanding biological processes and diseases. With the rapid development of high-throughput proteomics technologies, massive quantitative PTM proteome datasets have been generated. Thus, a comprehensive one-stop data resource for surfing big data will benefit the community. Here, we updated our previous phosphorylation dynamics database qPhos to the qPTM (http://qptm.omicsbio.info). In qPTM, 11 482 553 quantification events among six types of PTMs, including phosphorylation, acetylation, glycosylation, methylation, SUMOylation and ubiquitylation in four different organisms were collected and integrated, and the matched proteome datasets were included if available. The raw mass spectrometry based false discovery rate control and the recurrences of identifications among datasets were integrated into a scoring system to assess the reliability of the PTM sites. Browse and search functions were improved to facilitate users in swiftly and accurately acquiring specific information. The results page was revised with more abundant annotations, and time-course dynamics data were visualized in trend lines. We expected the qPTM database to be a much more powerful and comprehensive data repository for the PTM research community.

Quantitative Phosphoproteomics Analysis Uncovers PAK2- and CDK1-Mediated Malignant Signaling Pathways in Clear Cell Renal Cell Carcinoma

Clear cell Renal Cell Carcinoma (ccRCC) is among the 10 most common cancers in both men and women and causes more than 140,000 deaths worldwide every year. In order to elucidate the underlying molecular mechanisms orchestrated by phosphorylation modifications, we performed a comprehensive quantitative phosphoproteomics characterization of ccRCC tumor and normal adjacent tissues. Here, we identified 16,253 phosphopeptides, of which more than 9000 were singly quantified. Our in-depth analysis revealed 600 phosphopeptides to be significantly differentially regulated between tumor and normal tissues. Moreover, our data revealed that significantly up-regulated phosphoproteins are associated with protein synthesis and cytoskeletal re-organization which suggests proliferative and migratory behavior of renal tumors. This is supported by a mesenchymal profile of ccRCC phosphorylation events. Our rigorous characterization of the renal phosphoproteome also suggests that both epidermal growth factor receptor and vascular endothelial growth factor receptor are important mediators of phospho signaling in RCC pathogenesis. Furthermore, we determined the kinases p21-activated kinase 2, cyclin-dependent kinase 1 and c-Jun N-terminal kinase 1 to be master kinases that are responsible for phosphorylation of many substrates associated with cell proliferation, inflammation and migration. Moreover, high expression of p21-activated kinase 2 is associated with worse survival outcome of ccRCC patients. These master kinases are targetable by inhibitory drugs such as fostamatinib, minocycline, tamoxifen and bosutinib which can serve as novel therapeutic agents for ccRCC treatment.

FVC as an adaptive and accurate method for filtering variants from popular NGS analysis pipelines

The quality control of variants from whole-genome sequencing data is vital in clinical diagnosis and human genetics research. However, current filtering methods (Frequency, Hard-Filter, VQSR, GARFIELD, and VEF) were developed to be utilized on particular variant callers and have certain limitations. Especially, the number of eliminated true variants far exceeds the number of removed false variants using these methods. Here, we present an adaptive method for quality control on genetic variants from different analysis pipelines, and validate it on the variants generated from four popular variant callers (GATK HaplotypeCaller, Mutect2, Varscan2, and DeepVariant). FVC consistently exhibited the best performance. It removed far more false variants than the current state-of-the-art filtering methods and recalled ~51-99% true variants filtered out by the other methods. Once trained, FVC can be conveniently integrated into a user-specific variant calling pipeline.

FVC is a method for calling specific gene variants from whole genome data, for potential use in clinical diagnosis and human genetics research.

Proteotype coevolution and quantitative diversity across 11 mammalian species

Evolutionary profiling has been largely limited to the nucleotide level. Using consistent proteomic methods, we quantified proteomic and phosphoproteomic layers in fibroblasts from 11 common mammalian species, with transcriptomes as reference. Covariation analysis indicates that transcript and protein expression levels and variabilities across mammals remarkably follow functional role, with extracellular matrix-associated expression being the most variable, demonstrating strong transcriptome-proteome coevolution. The biological variability of gene expression is universal at both interindividual and interspecies scales but to a different extent. RNA metabolic processes particularly show higher interspecies versus interindividual variation. Our results further indicate that while the ubiquitin-proteasome system is strongly conserved in mammals, lysosome-mediated protein degradation exhibits remarkable variation between mammalian lineages. In addition, the phosphosite profiles reveal a phosphorylation coevolution network independent of protein abundance.

Arsenic perception and signaling: The yet unexplored world

Arsenic is one of the most potent carcinogens in the biosphere, jeopardizing the health of millions of people due to its entrance into the human food chain through arsenic-contaminated waters and staple crops, particularly rice. Although the mechanisms of arsenic sensing are widely known in yeast and bacteria, scientific evidence concerning arsenic sensors or components of early arsenic signaling in plants is still in its infancy. However, in recent years, we have gained understanding of the mechanisms involved in arsenic uptake and detoxification in different plant species and started to get insights into arsenic perception and signaling, which allows us to glimpse the possibility to design effective strategies to prevent arsenic accumulation in edible crops or to increase plant arsenic extraction for phytoremediation purposes. In this context, it has been recently described a mechanism according to which arsenite, the reduced form of arsenic, regulates the arsenate/phosphate transporter, consistent with the idea that arsenite functions as a selective signal that coordinates arsenate uptake with detoxification mechanisms. Additionally, several transcriptional and post-translational regulators, miRNAs and phytohormones involved in arsenic signaling and tolerance have been identified. On the other hand, studies concerning the developmental programs triggered to adapt root architecture in order to cope with arsenic toxicity are just starting to be disclosed. In this review, we compile and analyze the latest advances toward understanding how plants perceive arsenic and coordinate its acquisition with detoxification mechanisms and root developmental programs.

Substrate spectrum of PPM1D in the cellular response to DNA double-strand breaks

PPM1D is a p53-regulated protein phosphatase that modulates the DNA damage response (DDR) and is frequently altered in cancer. Here, we employed chemical inhibition of PPM1D and quantitative mass spectrometry-based phosphoproteomics to identify the substrates of PPM1D upon induction of DNA double-strand breaks (DSBs) by etoposide. We identified 73 putative PPM1D substrates that are involved in DNA repair, regulation of transcription, and RNA processing. One-third of DSB-induced S/TQ phosphorylation sites are dephosphorylated by PPM1D, demonstrating that PPM1D only partially counteracts ATM/ATR/DNA-PK signaling. PPM1D-targeted phosphorylation sites are found in a specific amino acid sequence motif that is characterized by glutamic acid residues, high intrinsic disorder, and poor evolutionary conservation. We identified a functionally uncharacterized protein Kanadaptin as ATM and PPM1D substrate upon DSB induction. We propose that PPM1D plays a role during the response to DSBs by regulating the phosphorylation of DNA- and RNA-binding proteins in intrinsically disordered regions.

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MS-based phosphoproteomic profiling of PPM1D substrates in U2OS and HCT116 cells

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PPM1D counteracts ATM in the cellular response to DNA double-strand breaks

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PPM1D target sites localize to glutamic acid-rich regions with high intrinsic disorder

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Kanadaptin is a putative DNA damage response factor regulated by ATM and PPM1D

Novel Online Three-Dimensional Separation Expands the Detectable Functional Landscape of Cellular Phosphoproteome

Protein phosphorylation is a prevalent post-translational modification that regulates essentially every aspect of cellular processes. Currently, liquid chromatography-tandem mass spectrometry (LC-MS/MS) with an extensive offline sample fractionation and a phosphopeptide enrichment method is a best practice for deep phosphoproteome profiling, but balancing throughput and profiling depth remains a practical challenge. We present an online three-dimensional separation method for ultradeep phosphoproteome profiling that combines an online two-dimensional liquid chromatography separation and an additional gas-phase separation. This method identified over 100,000 phosphopeptides (>60,000 phosphosites) in HeLa cells during 1.5 days of data acquisition, and the largest HeLa cell phosphoproteome significantly expanded the detectable functional landscape of cellular phosphoproteome.

mTORC1 controls Golgi architecture and vesicle secretion by phosphorylation of SCYL1

The protein kinase mechanistic target of rapamycin complex 1 (mTORC1) is a master regulator of cell growth and proliferation, supporting anabolic reactions and inhibiting catabolic pathways like autophagy. Its hyperactivation is a frequent event in cancer promoting tumor cell proliferation. Several intracellular membrane-associated mTORC1 pools have been identified, linking its function to distinct subcellular localizations. Here, we characterize the N-terminal kinase-like protein SCYL1 as a Golgi-localized target through which mTORC1 controls organelle distribution and extracellular vesicle secretion in breast cancer cells. Under growth conditions, SCYL1 is phosphorylated by mTORC1 on Ser754, supporting Golgi localization. Upon mTORC1 inhibition, Ser754 dephosphorylation leads to SCYL1 displacement to endosomes. Peripheral, dephosphorylated SCYL1 causes Golgi enlargement, redistribution of early and late endosomes and increased extracellular vesicle release. Thus, the mTORC1-controlled phosphorylation status of SCYL1 is an important determinant regulating subcellular distribution and function of endolysosomal compartments. It may also explain the pathophysiology underlying human genetic diseases such as CALFAN syndrome, which is caused by loss-of-function of SCYL1.

mTORC1 is a master regulator of cell growth with well-known functions in inhibiting autophagic vesicle formation. Here, the authors show that mTORC1 also affects Golgi architecture and vesicle secretion by phosphorylating the scaffold protein SCYL1.

Phosphoproteomics of primary AML patient samples reveals rationale for AKT combination therapy and p53 context to overcome selinexor resistance

Acute myeloid leukemia (AML) is a heterogeneous disease with variable patient responses to therapy. Selinexor, an inhibitor of nuclear export, has shown promising clinical activity for AML. To identify the molecular context for monotherapy sensitivity as well as rational drug combinations, we profile selinexor signaling responses using phosphoproteomics in primary AML patient samples and cell lines. Functional phosphosite scoring reveals that p53 function is required for selinexor sensitivity consistent with enhanced efficacy of selinexor in combination with the MDM2 inhibitor nutlin-3a. Moreover, combining selinexor with the AKT inhibitor MK-2206 overcomes dysregulated AKT-FOXO3 signaling in resistant cells, resulting in synergistic anti-proliferative effects. Using high-throughput spatial proteomics to profile subcellular compartments, we measure global proteome and phospho-proteome dynamics, providing direct evidence of nuclear translocation of FOXO3 upon combination treatment. Our data demonstrate the potential of phosphoproteomics and functional phosphorylation site scoring to successfully pinpoint key targetable signaling hubs for rational drug combinations.

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Phosphoproteomics with functional scoring uncovers context for selinexor sensitivity

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Functional p53 correlates with selinexor sensitivity, which is enhanced by nutlin-3a

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Dysregulated AKT-FOXO3 drives selinexor resistance, which is overcome with MK-2206

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Spatial proteomics reveals selinexor-induced nucleocytoplasmic protein shuttling

A field guide to the proteomics of post-translational modifications in DNA repair

All cells incur DNA damage from exogenous and endogenous sources and possess pathways to detect and repair DNA damage. Post-translational modifications (PTMs), in the past 20 years, have risen to ineluctable importance in the study of the regulation of DNA repair mechanisms. For example, DNA damage response kinases are critical in both the initial sensing of DNA damage as well as in orchestrating downstream activities of DNA repair factors. Mass spectrometry-based proteomics revolutionized the study of the role of PTMs in the DNA damage response and has canonized PTMs as central modulators of nearly all aspects of DNA damage signaling and repair. This review provides a biologist-friendly guide for the mass spectrometry analysis of PTMs in the context of DNA repair and DNA damage responses. We reflect on the current state of proteomics for exploring new mechanisms of PTM-based regulation and outline a roadmap for designing PTM mapping experiments that focus on the DNA repair and DNA damage responses.

Rapid and in-depth coverage of the (phospho-)proteome with deep libraries and optimal window design for dia-PASEF

Data-independent acquisition (DIA) methods have become increasingly attractive in mass spectrometry–based proteomics because they enable high data completeness and a wide dynamic range. Recently, we combined DIA with parallel accumulation–serial fragmentation (dia-PASEF) on a Bruker trapped ion mobility (IM) separated quadrupole time-of-flight mass spectrometer. This requires alignment of the IM separation with the downstream mass selective quadrupole, leading to a more complex scheme for dia-PASEF window placement compared with DIA. To achieve high data completeness and deep proteome coverage, here we employ variable isolation windows that are placed optimally depending on precursor density in the m/z and IM plane. This is implemented in the freely available py_diAID (Python package for DIA with an automated isolation design) package. In combination with in-depth project-specific proteomics libraries and the Evosep liquid chromatography system, we reproducibly identified over 7700 proteins in a human cancer cell line in 44 min with quadruplicate single-shot injections at high sensitivity. Even at a throughput of 100 samples per day (11 min liquid chromatography gradients), we consistently quantified more than 6000 proteins in mammalian cell lysates by injecting four replicates. We found that optimal dia-PASEF window placement facilitates in-depth phosphoproteomics with very high sensitivity, quantifying more than 35,000 phosphosites in a human cancer cell line stimulated with an epidermal growth factor in triplicate 21 min runs. This covers a substantial part of the regulated phosphoproteome with high sensitivity, opening up for extensive systems-biological studies.

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Optimal dia-PASEF window design with py_diAID combined with deep libraries.

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Quantification of the HeLa cell proteome to a depth of >7700 in only 44 min.

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Ion mobility–resolved phosphoproteomics determines >35,000 class I phosphosites.

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py_diAID is freely available as GUI, CLI, and Python modules.

The evolution of post-translational modifications

Post-translational modifications (PTMs) are chemical modifications that can regulate the activity and function of proteins. From an evolutionary perspective, they also represent a fast mechanism for the generation of phenotypic diversity and divergence. Advances in mass spectrometry have now enabled the identification of over 600 distinct PTM classes collectively spanning an order of 10⁶ unique sites. However, the chemical detection of PTMs has lagged far behind their functional characterisation, and relatively little is still known about the selective constraints that govern PTM evolution. In particular, the true fraction of PTM sites that are functional - and thus subject to selection - remains an open question. Here, I review advances made in the past two years towards understanding the evolution of PTMs and their associated enzymes.