Targeted suppression of mTORC2 reduces seizures across models of epilepsy

Epilepsy is a neurological disorder that poses a major threat to public health. Hyperactivation of mTOR complex 1 (mTORC1) is believed to lead to abnormal network rhythmicity associated with epilepsy, and its inhibition is proposed to provide some therapeutic benefit. However, mTOR complex 2 (mTORC2) is also activated in the epileptic brain, and little is known about its role in seizures. Here we discover that genetic deletion of mTORC2 from forebrain neurons is protective against kainic acid-induced behavioral and EEG seizures. Furthermore, inhibition of mTORC2 with a specific antisense oligonucleotide robustly suppresses seizures in several pharmacological and genetic mouse models of epilepsy. Finally, we identify a target of mTORC2, Nav1.2, which has been implicated in epilepsy and neuronal excitability. Our findings, which are generalizable to several models of human seizures, raise the possibility that inhibition of mTORC2 may serve as a broader therapeutic strategy against epilepsy.

Increased G3BP2-Tau interaction in tauopathies is a natural defense against Tau aggregation

Many RNA-binding proteins (RBPs), particularly those associated with RNA granules, promote pathological protein aggregation in neurodegenerative diseases. Here, we demonstrate that G3BP2, a core component of stress granules, directly interacts with Tau and inhibits Tau aggregation. In the human brain, the interaction of G3BP2 and Tau is dramatically increased in multiple tauopathies, and it is independent of neurofibrillary tangle (NFT) formation in Alzheimer's disease (AD). Surprisingly, Tau pathology is significantly elevated upon loss of G3BP2 in human neurons and brain organoids. Moreover, we found that G3BP2 masks the microtubule-binding region (MTBR) of Tau, thereby inhibiting Tau aggregation. Our study defines a novel role for RBPs as a line of defense against Tau aggregation in tauopathies.

RWD-derived response in multiple myeloma

Real-world data (RWD) are important for understanding the treatment course and response patterns of patients with multiple myeloma. This exploratory pilot study establishes a way to reliably assess response from incomplete laboratory measurements captured in RWD. A rule-based algorithm, adapted from International Myeloma Working Group response criteria, was used to derive response using RWD. This derived response (dR) algorithm was assessed using data from the phase III BELLINI trial, comparing the number of responders and non-responders assigned by independent review committee (IRC) versus the dR algorithm. To simulate a real-world scenario with missing data, a sensitivity analysis was conducted whereby available laboratory measurements in the dataset were artificially reduced. Associations between dR and overall survival were evaluated at 1) individual level and 2) treatment level in a real-world patient cohort obtained from a nationwide electronic health record-derived de-identified database. The algorithm's assignment of responders was highly concordant with that of the IRC (Cohen's Kappa 0.83) using the BELLINI data. The dR replicated the differences in overall response rate between the intervention and placebo arms reported in the trial (odds ratio 2.1 vs. 2.3 for IRC vs. dR assessment, respectively). Simulation of missing data in the sensitivity analysis (-50% of available laboratory measurements and -75% of urine monoclonal protein measurements) resulted in a minor reduction in the algorithm's accuracy (Cohen's Kappa 0.75). In the RWD cohort, dR was significantly associated with overall survival at all landmark times (hazard ratios 0.80-0.81, p<0.001) at the individual level, while the overall association was R2 = 0.67 (p<0.001) at the treatment level. This exploratory pilot study demonstrates the feasibility of deriving accurate response from RWD. With further confirmation in independent cohorts, the dR has the potential to be used as an endpoint in real-world studies and as a comparator in single-arm clinical trials.

DUSP4 protects BRAF- and NRAS-mutant melanoma from oncogene overdose through modulation of MITF

MAPK inhibitors (MAPKi) remain an important component of the standard of care for metastatic melanoma. However, acquired resistance to these drugs limits their therapeutic benefit. Tumor cells can become refractory to MAPKi by reactivation of ERK. When this happens, tumors often become sensitive to drug withdrawal. This drug addiction phenotype results from the hyperactivation of the oncogenic pathway, a phenomenon commonly referred to as oncogene overdose. Several feedback mechanisms are involved in regulating ERK signaling. However, the genes that serve as gatekeepers of oncogene overdose in mutant melanoma remain unknown. Here, we demonstrate that depletion of the ERK phosphatase, DUSP4, leads to toxic levels of MAPK activation in both drug-naive and drug-resistant mutant melanoma cells. Importantly, ERK hyperactivation is associated with down-regulation of lineage-defining genes including MITF Our results offer an alternative therapeutic strategy to treat mutant melanoma patients with acquired MAPKi resistance and those unable to tolerate MAPKi.

A cross-species spatiotemporal proteomic analysis identifies UBE3A-dependent signaling pathways and targets

Angelman syndrome (AS) is a severe neurodevelopmental disorder caused by the loss of neuronal E3 ligase UBE3A. Restoring UBE3A levels is a potential disease-modifying therapy for AS and has recently entered clinical trials. There is paucity of data regarding the molecular changes downstream of UBE3A hampering elucidation of disease therapeutics and biomarkers. Notably, UBE3A plays an important role in the nucleus but its targets have yet to be elucidated. Using proteomics, we assessed changes during postnatal cortical development in an AS mouse model. Pathway analysis revealed dysregulation of proteasomal and tRNA synthetase pathways at all postnatal brain developmental stages, while synaptic proteins were altered in adults. We confirmed pathway alterations in an adult AS rat model across multiple brain regions and highlighted region-specific differences. UBE3A reinstatement in AS model mice resulted in near complete and partial rescue of the proteome alterations in adolescence and adults, respectively, supporting the notion that restoration of UBE3A expression provides a promising therapeutic option. We show that the nuclear enriched transketolase (TKT), one of the most abundantly altered proteins, is a novel direct UBE3A substrate and is elevated in the neuronal nucleus of rat brains and human iPSC-derived neurons. Taken together, our study provides a comprehensive map of UBE3A-driven proteome remodeling in AS across development and species, and corroborates an early UBE3A reinstatement as a viable therapeutic option. To support future disease and biomarker research, we present an accessible large-scale multi-species proteomic resource for the AS community ( https://www.angelman-proteome-project.org/ ).

SILAC-based quantitative proteomics using mass spectrometry quantifies endoplasmic reticulum stress in whole HeLa cells

The unfolded protein response (UPR) involves extensive proteome remodeling in many cellular compartments. To date, a comprehensive analysis of the UPR has not been possible because of technological limitations. Here, we employ stable isotope labeling with amino acids in cell culture (SILAC)-based proteomics to quantify the response of over 6200 proteins to increasing concentrations of tunicamycin in HeLa cells. We further compare the effects of tunicamycin (5 µg/ml) to those of thapsigargin (1 µM) and DTT (2 mM), both activating the UPR through different mechanisms. This systematic quantification of the proteome-wide expression changes that follow proteostatic stress is a resource for the scientific community, enabling the discovery of novel players involved in the pathophysiology of the broad range of disorders linked to proteostasis. We identified increased expression in 38 proteins not previously linked to the UPR, of which 15 likely remediate ER stress, and the remainder may contribute to pathological outcomes. Unexpectedly, there are few strongly downregulated proteins, despite expression of the pro-apoptotic transcription factor CHOP, suggesting that IRE1-dependent mRNA decay (RIDD) has a limited contribution to ER stress-mediated cell death in our system.

Summary: A novel observation point of a familiar scenario: proteomic quantification of over 6200 proteins as a resource to further explore endoplasmic reticulum stress.

A Mass Spectrometry-Based Approach for Mapping Protein Subcellular Localization Reveals the Spatial Proteome of Mouse Primary Neurons

We previously developed a mass spectrometry-based method, dynamic organellar maps, for the determination of protein subcellular localization and identification of translocation events in comparative experiments. The use of metabolic labeling for quantification (stable isotope labeling by amino acids in cell culture [SILAC]) renders the method best suited to cells grown in culture. Here, we have adapted the workflow to both label-free quantification (LFQ) and chemical labeling/multiplexing strategies (tandem mass tagging [TMT]). Both methods are highly effective for the generation of organellar maps and capture of protein translocations. Furthermore, application of label-free organellar mapping to acutely isolated mouse primary neurons provided subcellular localization and copy-number information for over 8,000 proteins, allowing a detailed analysis of organellar organization. Our study extends the scope of dynamic organellar maps to any cell type or tissue and also to high-throughput screening.

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High-resolution organellar maps with label-free quantification (LFQ)

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High-throughput organellar maps with TMT-based multiplexing

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Deep mapping of EGF-induced protein localization changes with SILAC, LFQ, and TMT

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A quantitative spatial proteome from mouse primary neurons

Perseus: A Bioinformatics Platform for Integrative Analysis of Proteomics Data in Cancer Research

Mass spectrometry-based proteomics is a continuously growing field marked by technological and methodological improvements. Cancer proteomics is aimed at pursuing goals such as accurate diagnosis, patient stratification, and biomarker discovery, relying on the richness of information of quantitative proteome profiles. Translating these high-dimensional data into biological findings of clinical importance necessitates the use of robust and powerful computational tools and methods. In this chapter, we provide a detailed description of standard analysis steps for a clinical proteomics dataset performed in Perseus, a software for functional analysis of large-scale quantitative omics data.

Proteomics reveals the effects of sustained weight loss on the human plasma proteome

Sustained weight loss is a preferred intervention in a wide range of metabolic conditions, but the effects on an individual's health state remain ill‐defined. Here, we investigate the plasma proteomes of a cohort of 43 obese individuals that had undergone 8 weeks of 12% body weight loss followed by a year of weight maintenance. Using mass spectrometry‐based plasma proteome profiling, we measured 1,294 plasma proteomes. Longitudinal monitoring of the cohort revealed individual‐specific protein levels with wide‐ranging effects of losing weight on the plasma proteome reflected in 93 significantly affected proteins. The adipocyte‐secreted SERPINF1 and apolipoprotein APOF1 were most significantly regulated with fold changes of −16% and +37%, respectively (P < 10⁻¹³), and the entire apolipoprotein family showed characteristic differential regulation. Clinical laboratory parameters are reflected in the plasma proteome, and eight plasma proteins correlated better with insulin resistance than the known marker adiponectin. Nearly all study participants benefited from weight loss regarding a ten‐protein inflammation panel defined from the proteomics data. We conclude that plasma proteome profiling broadly evaluates and monitors intervention in metabolic diseases.

The MaxQuant computational platform for mass spectrometry-based shotgun proteomics

MaxQuant is one of the most frequently used platforms for mass-spectrometry (MS)-based proteomics data analysis. Since its first release in 2008, it has grown substantially in functionality and can be used in conjunction with more MS platforms. Here we present an updated protocol covering the most important basic computational workflows, including those designed for quantitative label-free proteomics, MS1-level labeling and isobaric labeling techniques. This protocol presents a complete description of the parameters used in MaxQuant, as well as of the configuration options of its integrated search engine, Andromeda. This protocol update describes an adaptation of an existing protocol that substantially modifies the technique. Important concepts of shotgun proteomics and their implementation in MaxQuant are briefly reviewed, including different quantification strategies and the control of false-discovery rates (FDRs), as well as the analysis of post-translational modifications (PTMs). The MaxQuant output tables, which contain information about quantification of proteins and PTMs, are explained in detail. Furthermore, we provide a short version of the workflow that is applicable to data sets with simple and standard experimental designs. The MaxQuant algorithms are efficiently parallelized on multiple processors and scale well from desktop computers to servers with many cores. The software is written in C# and is freely available at http://www.maxquant.org.

Integrative proteomic profiling of ovarian cancer cell lines reveals precursor cell associated proteins and functional status

A cell line representative of human high-grade serous ovarian cancer (HGSOC) should not only resemble its tumour of origin at the molecular level, but also demonstrate functional utility in pre-clinical investigations. Here, we report the integrated proteomic analysis of 26 ovarian cancer cell lines, HGSOC tumours, immortalized ovarian surface epithelial cells and fallopian tube epithelial cells via a single-run mass spectrometric workflow. The in-depth quantification of >10,000 proteins results in three distinct cell line categories: epithelial (group I), clear cell (group II) and mesenchymal (group III). We identify a 67-protein cell line signature, which separates our entire proteomic data set, as well as a confirmatory publicly available CPTAC/TCGA tumour proteome data set, into a predominantly epithelial and mesenchymal HGSOC tumour cluster. This proteomics-based epithelial/mesenchymal stratification of cell lines and human tumours indicates a possible origin of HGSOC either from the fallopian tube or from the ovarian surface epithelium.

High-grade serous ovarian cancer is the most common and aggressive ovarian cancer, with uncertain cell of origin. Here, the authors undertake a mass spectrometric analysis of 26 cancer cell lines and identify a protein signature that classifies ovarian cancer tissues into epithelial and mesenchymal groups.

Global, quantitative and dynamic mapping of protein subcellular localization

Subcellular localization critically influences protein function, and cells control protein localization to regulate biological processes. We have developed and applied Dynamic Organellar Maps, a proteomic method that allows global mapping of protein translocation events. We initially used maps statically to generate a database with localization and absolute copy number information for over 8700 proteins from HeLa cells, approaching comprehensive coverage. All major organelles were resolved, with exceptional prediction accuracy (estimated at >92%). Combining spatial and abundance information yielded an unprecedented quantitative view of HeLa cell anatomy and organellar composition, at the protein level. We subsequently demonstrated the dynamic capabilities of the approach by capturing translocation events following EGF stimulation, which we integrated into a quantitative model. Dynamic Organellar Maps enable the proteome-wide analysis of physiological protein movements, without requiring any reagents specific to the investigated process, and will thus be widely applicable in cell biology.

DOI:http://dx.doi.org/10.7554/eLife.16950.001

The interior of every cell is highly organised, and contains many compartments, called organelles, that are dedicated to specific roles. Proteins are the tools and machines of the cell, and each organelle has its own set of proteins that it requires to work correctly. Each cell contains ten or more organelles, and several thousand different types of proteins. The exact location of proteins in the cell is important; once we know what compartment a protein is in, it is easier to narrow down what it might be doing.

The location of many proteins in a cell is unclear or simply not known. Moreover, since changing the location of a protein can change its activity, it is also important to be able to detect changes in the location of proteins under different circumstances, such as before and after drug treatment.

Itzhak et al. set out to develop a method that reveals the locations of all the proteins in a cell at any given time. The resulting technique maps the location of most of the proteins in a human cancer cell line and, in addition, determines how many copies of each protein there are. Combining these two types of information produces a model of the cell’s architecture. Importantly, Itzhak et al. were able to compare such a model of the cell under normal circumstances to a model made after the cell had been stimulated with a growth factor. This revealed which proteins had changed location, identifying these proteins as important for the cell’s response to the growth factor.

The new mapping method could be used in the future to analyse the anatomy of different cell types, such as nerve cells and cells of the immune system. Itzhak et al. also want to investigate the differences between healthy cells and cells from people with neurological disorders to understand how such diseases arise.

DOI:http://dx.doi.org/10.7554/eLife.16950.002

The Proteome of Primary Prostate Cancer

BACKGROUND

Clinical management of the prostate needs improved prognostic tests and treatment strategies. Because proteins are the ultimate effectors of most cellular reactions, are targets for drug actions and constitute potential biomarkers; a quantitative systemic overview of the proteome changes occurring during prostate cancer (PCa) initiation and progression can result in clinically relevant discoveries.

OBJECTIVES

To study cellular processes altered in PCa using system-wide quantitative analysis of changes in protein expression in clinical samples and to identify prognostic biomarkers for disease aggressiveness.

DESIGN, SETTING, AND PARTICIPANTS

Mass spectrometry was used for genome-scale quantitative proteomic profiling of 28 prostate tumors (Gleason score 6-9) and neighboring nonmalignant tissue in eight cases, obtained from formalin-fixed paraffin-embedded prostatectomy samples. Two independent cohorts of PCa patients (summing 752 cases) managed by expectancy were used for immunohistochemical evaluation of proneuropeptide-Y (pro-NPY) as a prognostic biomarker.

RESULTS AND LIMITATIONS

Over 9000 proteins were identified as expressed in the human prostate. Tumor tissue exhibited elevated expression of proteins involved in multiple anabolic processes including fatty acid and protein synthesis, ribosomal biogenesis and protein secretion but no overt evidence of increased proliferation was observed. Tumors also showed increased levels of mitochondrial proteins, which was associated with elevated oxidative phosphorylation capacity measured in situ. Molecular analysis indicated that some of the proteins overexpressed in tumors, such as carnitine palmitoyltransferase 2 (CPT2, fatty acid transporter), coatomer protein complex, subunit alpha (COPA, vesicle secretion), and mitogen- and stress-activated protein kinase 1 and 2 (MSK1/2, protein kinase) regulate the proliferation of PCa cells. Additionally, pro-NPY was found overexpressed in PCa (5-fold, p<0.05), but largely absent in other solid tumor types. Pro-NPY expression, alone or in combination with the ERG status of the tumor, was associated with an increased risk of PCa specific mortality, especially in patients with Gleason score ≤ 7 tumors.

CONCLUSIONS

This study represents the first system-wide quantitative analysis of proteome changes associated to localized prostate cancer and as such constitutes a valuable resource for understanding the complex metabolic changes occurring in this disease. We also demonstrated that pro-NPY, a protein that showed differential expression between high and low risk tumors in our proteomic analysis, is also a PCa specific prognostic biomarker associated with increased risk for disease specific death in patients carrying low risk tumors.

PATIENT SUMMARY

The identification of proteins whose expression change in prostate cancer provides novel mechanistic information related to the disease etiology. We hope that future studies will prove the value of this proteome dataset for development of novel therapies and biomarkers.

Machine Learning-based Classification of Diffuse Large B-cell Lymphoma Patients by Their Protein Expression Profiles

Characterization of tumors at the molecular level has improved our knowledge of cancer causation and progression. Proteomic analysis of their signaling pathways promises to enhance our understanding of cancer aberrations at the functional level, but this requires accurate and robust tools. Here, we develop a state of the art quantitative mass spectrometric pipeline to characterize formalin-fixed paraffin-embedded tissues of patients with closely related subtypes of diffuse large B-cell lymphoma. We combined a super-SILAC approach with label-free quantification (hybrid LFQ) to address situations where the protein is absent in the super-SILAC standard but present in the patient samples. Shotgun proteomic analysis on a quadrupole Orbitrap quantified almost 9,000 tumor proteins in 20 patients. The quantitative accuracy of our approach allowed the segregation of diffuse large B-cell lymphoma patients according to their cell of origin using both their global protein expression patterns and the 55-protein signature obtained previously from patient-derived cell lines (Deeb, S. J., D'Souza, R. C., Cox, J., Schmidt-Supprian, M., and Mann, M. (2012) Mol. Cell. Proteomics 11, 77–89). Expression levels of individual segregation-driving proteins as well as categories such as extracellular matrix proteins behaved consistently with known trends between the subtypes. We used machine learning (support vector machines) to extract candidate proteins with the highest segregating power. A panel of four proteins (PALD1, MME, TNFAIP8, and TBC1D4) is predicted to classify patients with low error rates. Highly ranked proteins from the support vector analysis revealed differential expression of core signaling molecules between the subtypes, elucidating aspects of their pathobiology.

Visualization of LC-MS/MS proteomics data in MaxQuant

Modern software platforms enable the analysis of shotgun proteomics data in an automated fashion resulting in high quality identification and quantification results. Additional understanding of the underlying data can be gained with the help of advanced visualization tools that allow for easy navigation through large LC‐MS/MS datasets potentially consisting of terabytes of raw data. The updated MaxQuant version has a map navigation component that steers the users through mass and retention time‐dependent mass spectrometric signals. It can be used to monitor a peptide feature used in label‐free quantification over many LC‐MS runs and visualize it with advanced 3D graphic models. An expert annotation system aids the interpretation of the MS/MS spectra used for the identification of these peptide features.

Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling

Regulatory protein phosphorylation controls normal and pathophysiological signaling in eukaryotic cells. Despite great advances in mass-spectrometry-based proteomics, the extent, localization, and site-specific stoichiometry of this posttranslational modification (PTM) are unknown. Here, we develop a stringent experimental and computational workflow, capable of mapping more than 50,000 distinct phosphorylated peptides in a single human cancer cell line. We detected more than three-quarters of cellular proteins as phosphoproteins and determined very high stoichiometries in mitosis or growth factor signaling by label-free quantitation. The proportion of phospho-Tyr drastically decreases as coverage of the phosphoproteome increases, whereas Ser/Thr sites saturate only for technical reasons. Tyrosine phosphorylation is maintained at especially low stoichiometric levels in the absence of specific signaling events. Unexpectedly, it is enriched on higher-abundance proteins, and this correlates with the substrate KM values of tyrosine kinases. Our data suggest that P-Tyr should be considered a functionally separate PTM of eukaryotic proteomes.

MaxQuant for in-depth analysis of large SILAC datasets

Proteomics experiments can generate very large volumes of data, in particular in situations where within one experimental design many samples are compared to each other, possibly in combination with pre-fractionation of samples prior to LC-MS analysis. Here we provide a step-by-step protocol explaining how the current MaxQuant version can be used to analyze large SILAC-labeling datasets in an efficient way.