Publications

Despite the central role of T cells in tumor immunity, attempts to harness their cytotoxic capacity as a therapy have met limited efficacy, partially as a result of the suppressive microenvironment which limits their migration and activation. In contrast, myeloid cells massively infiltrate tumors and are well adapted to survive these harsh conditions. While they are equipped with cell-killing abilities, they often adopt an immunosuppressive phenotype upon migration to tumors. Therefore, the questions of how to modify their activation programming against cancer, and what signaling cascades should be activated in myeloid cells to elicit their cytotoxicity have remained unclear. Here, we found that activation of IgM-induced signaling in murine myeloid cells results in secretion of lytic granules and massive tumor cell death. These findings open venues for designing novel immunotherapy by equipping monocytes with chimeric receptors that target tumor antigens and consequently, signal through IgM receptor. Nonetheless, we found that myeloid cells do not express the antibody-derived portion used to recognize the tumor antigen due to the induction of an ER stress response. To overcome this limitation, we designed chimeric receptors that are based on the high-affinity FcγRI for IgG. Incubation of macrophages expressing these receptors along with tumor-binding IgG induced massive tumor cell killing and secretion of reactive oxygen species and Granzyme B. Overall, this work highlights the challenges involved in genetically reprogramming the signaling in myeloid cells and provides a framework for endowing myeloid cells with antigen-specific cytotoxicity.

Glioblastoma (GB) is the most aggressive neoplasm of the brain. Poor prognosis is mainly attributed to tumor heterogeneity, invasiveness and drug resistance. Only a small fraction of GB patients survives longer than 24 months from the time of diagnosis (ie, long-term survivors [LTS]). In our study, we aimed to identify molecular markers associated with favorable GB prognosis as a basis to develop therapeutic applications to improve patients' outcome. We have recently assembled a proteogenomic dataset of 87 GB clinical samples of varying survival rates. Following RNA-seq and mass spectrometry (MS)-based proteomics analysis, we identified several differentially expressed genes and proteins, including some known cancer-related pathways and some less established that showed higher expression in short-term (

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

The development of mass spectrometry-based single-cell proteomics technologies opens unique opportunities to understand the functional crosstalk between cells that drive tumor development.

Exercise prevents cancer incidence and recurrence, yet the underlying mechanism behind this relationship remains mostly unknown. Here we report that exercise induces metabolic reprogramming of internal organs that increases nutrient demand and protects against metastatic colonization by limiting nutrient availability to the tumor, generating an exercise-induced metabolic shield. Proteomic and ex vivo metabolic capacity analyses of murine internal organs revealed that exercise induces catabolic processes, glucose uptake, mitochondrial activity, and GLUT expression. Proteomic analysis of routinely active human subject plasma demonstrated increased carbohydrate utilization following exercise. Epidemiological data from a 20-year prospective study of a large human cohort of initially cancer-free participants revealed that exercise prior to cancer initiation had a modest impact on cancer incidence in low metastatic stages but significantly reduced the likelihood of highly metastatic cancer. In three models of melanoma in mice, exercise prior to cancer injection significantly protected against metastases in distant organs. The protective effects of exercise were dependent on mTOR activity, and inhibition of the mTOR pathway with rapamycin treatment ex vivo reversed the exercise-induced metabolic shield. Under limited glucose conditions, active stroma consumed significantly more glucose at the expense of the tumor. Collectively, these data suggest a clash between the metabolic plasticity of cancer and exercise-induced metabolic reprogramming of the stroma, raising an opportunity to block metastasis by challenging the metabolic needs of the tumor.

Single-cell analyses characterize individual cells, allowing their clustering and characterization in an unsupervised manner. Single-cell genomics and transcriptomics dominate the field of single-cell analysis, however, these often do not accurately reflect cellular functions. In contrast, single-cell protein analyses were until recently only performed using antibody-based approaches. This review aims to highlight the recent developments in mass spectrometry-based single-cell proteomics and discuss the challenges and opportunities. Advances in the field hold the promise to impact biomedical research and contribute to the understanding of complex biological systems.

Tumor relapse as a consequence of chemotherapy resistance is a major clinical challenge in advanced stage breast tumors. To identify processes associated with poor clinical outcome, we took a mass spectrometrybased proteomic approach and analyzed a breast cancer cohort of 113 formalinfixed paraffinembedded samples. Proteomic profiling of matched tumors before and after chemotherapy, and tumoradjacent normal tissue, all from the same patients, allowed us to define eight patterns of protein level changes, two of which correlate to better chemotherapy response. Supervised analysis identified two proteins of proline biosynthesis pathway, PYCR1 and ALDH18A1, that were significantly associated with resistance to treatment based on pattern dominance. Weighted gene correlation network analysis of posttreatment samples revealed that these proteins are associated with tumor relapse and affect patient survival. Functional analysis showed that knockdown of PYCR1 reduced invasion and migration capabilities of breast cancer cell lines. PYCR1 knockout significantly reduced tumor burden and increased drug sensitivity of orthotopically injected ERpositive tumor in vivo, thus emphasizing the role of PYCR1 in resistance to chemotherapy.

Cancer-specific metabolic phenotypes and their vulnerabilities represent a viable area of cancer research. In this study, we explored the association of breast cancer subtypes with different metabolic phenotypes and identified isocitrate dehydrogenase 2 (IDH2) as a key player in triple-negative breast cancer (TNBC) and HER2. Functional assays combined with mass spectrometry-based analyses revealed the oncogenic role of IDH2 in cell proliferation, anchorage-independent growth, glycolysis, mitochondrial respiration, and antioxidant defense. Genome-scale metabolic modeling identified phosphoglycerate dehydrogenase (PHGDH) and phosphoserine aminotransferase (PSAT1) as the synthetic dosage lethal (SDL) partners of IDH2. In agreement, CRISPR-Cas9 knockout of PHGDH and PSAT1 showed the essentiality of serine biosynthesis proteins in IDH2-high cells. The clinical significance of the SDL interaction was supported by patients with IDH2-high/PHGDH-low tumors, who exhibited longer survival than patients with IDH2-high/PHGDH-high tumors. Furthermore, PHGDH inhibitors were effective in treating IDH2-high cells

Extensive tumour inflammation, which is reflected by high levels of infiltrating T cells and interferon-γ (IFNγ) signalling, improves the response of patients with melanoma to checkpoint immunotherapy. Many tumours, however, escape by activating cellular pathways that lead to immunosuppression. One such mechanism is the production of tryptophan metabolites along the kynurenine pathway by the enzyme indoleamine 2,3-dioxygenase 1 (IDO1), which is induced by IFNγ. However, clinical trials using inhibition of IDO1 in combination with blockade of the PD1 pathway in patients with melanoma did not improve the efficacy of treatment compared to PD1 pathway blockade alone, pointing to an incomplete understanding of the role of IDO1 and the consequent degradation of tryptophan in mRNA translation and cancer progression. Here we used ribosome profiling in melanoma cells to investigate the effects of prolonged IFNγ treatment on mRNA translation. Notably, we observed accumulations of ribosomes downstream of tryptophan codons, along with their expected stalling at the tryptophan codon. This suggested that ribosomes bypass tryptophan codons in the absence of tryptophan. A detailed examination of these tryptophan-associated accumulations of ribosomes-which we term 'W-bumps'-showed that they were characterized by ribosomal frameshifting events. Consistently, reporter assays combined with proteomic and immunopeptidomic analyses demonstrated the induction of ribosomal frameshifting, and the generation and presentation of aberrant trans-frame peptides at the cell surface after treatment with IFNγ. Priming of naive T cells from healthy donors with aberrant peptides induced peptide-specific T cells. Together, our results suggest that IDO1-mediated depletion of tryptophan, which is induced by IFNγ, has a role in the immune recognition of melanoma cells by contributing to diversification of the peptidome landscape.

Stress granules (SGs) are cytoplasmic assemblies of proteins and non-translating mRNAs. Whereas much has been learned about SG formation, a major gap remains in understanding the compositional changes SGs undergo during normal disassembly and under disease conditions. Here, we address this gap by proteomic dissection of the SG temporal disassembly sequence using multi-bait APEX proximity proteomics. We discover 109 novel SG proteins and characterize distinct SG substructures. We reveal dozens of disassembly-engaged proteins (DEPs), some of which play functional roles in SG disassembly, including small ubiquitin-like modifier (SUMO) conjugating enzymes. We further demonstrate that SUMOylation regulates SG disassembly and SG formation. Parallel proteomics with amyotrophic lateral sclerosis (ALS)-associated C9ORF72 dipeptides uncovered attenuated DEP recruitment during SG disassembly and impaired SUMOylation. Accordingly, SUMO activity ameliorated C9ORF72-ALS-related neurodegeneration in Drosophila. By dissecting the SG spatiotemporal proteomic landscape, we provide an in-depth resource for future work on SG function and reveal basic and disease-relevant mechanisms of SG disassembly.

Background: Numerous case studies have reported spontaneous regression of recognized metastases following primary tumor excision, but underlying mechanisms are elusive. Here, we present a model of regression and latency of metastases following primary tumor excision and identify potential underlying mechanisms. Results: Using MDA-MB-231^HM human breast cancer cells that express highly sensitive luciferase, we monitored early development stages of spontaneous metastases in BALB/c nu/nu mice. Removal of the primary tumor caused marked regression of micro-metastases, but not of larger metastases, and in vivo supplementation of tumor secretome diminished this regression, suggesting that primary tumor-secreted factors promote early metastatic growth. Correspondingly, MDA-MB-231^HM-conditioned medium increased in vitro tumor proliferation and adhesion and reduced apoptosis. To identify specific mediating factors, cytokine array and proteomic analysis of MDA-MB-231^HM secretome were conducted. The results identified significant enrichment of angiogenesis, growth factor binding and activity, focal adhesion, and metalloprotease and apoptosis regulation processes. Neutralization of MDA-MB-231^HM-secreted key mediators of these processes, IL-8, PDGF-AA, Serpin E1 (PAI-1), and MIF, each antagonized secretome-induced proliferation. Moreover, their in vivo simultaneous blockade in the presence of the primary tumor arrested the development of micro-metastases. Interestingly, in the METABRIC cohort of breast cancer patients, elevated expression of Serpin E1, IL-8, or the four factors combined predicted poor survival. Conclusions: These results demonstrate regression and latency of micro-metastases following primary tumor excision and a crucial role for primary tumor secretome in promoting early metastatic growth in MDA-MB-231^HM xenografts. If generalized, such findings can suggest novel approaches to control micro-metastases and minimal residual disease.

In this issue of Cell, articles by Gillette et al., Chen et al., and Xu, et al. collectively provide a deep and comprehensive proteogenomic analysis of lung adenocarcinoma, addressing differences in patient ethnicity and smoking background. They highlight the importance of associating genomics with the functional proteomic outcome.

MicroRNAs (miR) are small RNA molecules that shape the cell transcriptome and proteome through regulation of mRNA stability and translation. Here, we examined their function as determinants of cell resistance to complement-dependent cytotoxicity (CDC). To achieve this goal, we compared the expression of microRNAs between complement-resistant and -sensitive K562 leukemia, Raji lymphoma, and HCT-116 colorectal carcinoma cells. Global microRNA array analysis identified miR-150, miR-328, and miR-616 as regulators of CDC resistance. Inhibition of miR-150 reduced resistance, whereas inhibition of miR-328 or miR-616 enhanced cell resistance. Treatment of K562 cells with a sublytic dose of complement was shown to rapidly increase miR-150, miR-328, and miR-616 expression. Protein targets of these microRNAs were analyzed in K562 cells by mass spectrometry-based proteomics. Expression of the complement membrane regulatory proteins CD46 and CD59 was significantly enhanced after inhibition of miR-328 and miR-616. Enrichment of proteins of mitochondria, known target organelles in CDC, was observed after miR-150, miR-328, and miR-616 inhibition. In conclusion, miR-150, miR-328, and miR-616 regulate cell resistance to CDC by modifying the expression of the membrane complement regulators CD46 and CD59 and the response of the mitochondria to complement lytic attack. These microRNAs may be considered targets for intervention in complement-associated diseases and in anticancer, complement-based therapy.

The translation machinery and the genes it decodes co-evolved to achieve production throughput and accuracy. Nonetheless, translation errors are frequent, and they affect physiology and protein evolution. Mapping translation errors in proteomes and understanding their causes is hindered by lack of a proteome-wide experimental methodology. We present the first methodology for systematic detection and quantification of errors in entire proteomes. Following proteome mass spectrometry, we identify, in E. coli and yeast, peptides whose mass indicates specific amino acid substitutions. Most substitutions result from codon-anticodon mispairing. Errors occur at sites that evolve rapidly and that minimally affect energetic stability, indicating selection for high translation fidelity. Ribosome density data show that errors occur at sites where ribosome velocity is higher, demonstrating a trade-off between speed and accuracy. Treating bacteria with an aminoglycoside antibiotic or deprivation of specific amino acids resulted in particular patterns of errors. These results reveal a mechanistic and evolutionary basis for translation fidelity.

Firing rate set-point regulation has puzzled researchers for decades. Our findings show that mitochondrial DHODH meets the criteria of a bone fide regulator of activity set points and suggest lowering firing set point as a new strategy to treat epilepsy.

Integrating different data types to answer biological questions is a challenging problem, which can, however, provide stronger insights than using each dataset separately. ModulOmics is a statistical framework to integrate multiple omics data types and various statistical tests into one probabilistic model, with the aim of identifying functionally connected modules. It simultaneously (rather than sequentially)optimizes all tests and efficiently searches the large candidates space with a two-step optimization procedure. Across cancer types, ModulOmics identifies key modules representing cancer-related mechanisms.

Obesity has become one of the leading pathophysiologic disorders in recent years. Adipose tissue is the main tissue related to obesity and is known to play a role in various physiological complications, including type 2 diabetes. To better understand how the fat tissue develops, we used an in vitro live cell imaging system to quantify the adipogenesis by means of nondestructive digital imaging to monitor the accumulation of intracellular lipid droplets (LDs), a hallmark of adipogenesis, from the macro- to the micro-scale. Analyzing the cells' shape at the single-cell level allows to quantify the cells' shape change from a fibroblast to spherical morphology, indicating the start of adipogenesis. To reveal the molecular alterations, we applied a proteomic approach using high-resolution mass spectrometry of the proliferation, confluent fibroblasts and of adipocytes. During this process, we noted the reorganization of the cells' extracellular matrix (ECM) network microenvironment from fibrillary collagen types I, III and V to collagens IV and VI, which affected the cells niche. The changes in ECM are translated for cytoskeleton remodeling according to cell fate-determining mechanisms. We quantified the cytoskeleton rearrangement of long oriented actin fibers or short cortical and disorganized fibers, associated with LDs accumulation in adipocytes. Developing in vitro models and analytical methods enable us to study differentiation into adipocytes that will advance our understanding regarding the niche conditions that affect adipogenesis. Consequently, this will enable the development of new modalities to prevent obesity and its deleterious outcomes and to develop potential treatments to battle pathophysiology-related diseases.

Bone is the most predominant site of distant metastasis in prostate cancer, and patients have limited therapeutic options at this stage.

The DNA damage response (DDR) is an extensive signaling network that is robustly mobilized by DNA double-strand breaks (DSBs). The primary transducer of the DSB response is the protein kinase, ataxia-telangiectasia, mutated (ATM). Here, we establish nuclear poly(A)-binding protein 1 (PABPN1) as a novel target of ATM and a crucial player in the DSB response. PABPN1 usually functions in regulation of RNA processing and stability. We establish that PABPN1 is recruited to the DDR as a critical regulator of DSB repair. A portion of PABPN1 relocalizes to DSB sites and is phosphorylated on Ser95 in an ATM-dependent manner. PABPN1 depletion sensitizes cells to DSB-inducing agents and prolongs the DSB-induced G2/M cell-cycle arrest, and DSB repair is hampered by PABPN1 depletion or elimination of its phosphorylation site. PABPN1 is required for optimal DSB repair via both nonhomologous end-joining (NHEJ) and homologous recombination repair (HRR), and specifically is essential for efficient DNA-end resection, an initial, key step in HRR. Using mass spectrometry analysis, we capture DNA damage-induced interactions of phospho-PABPN1, including well-established DDR players as well as other RNA metabolizing proteins. Our results uncover a novel ATM-dependent axis in the rapidly growing interface between RNA metabolism and the DDR.

Eukaryotic translation initiation factor 2B (eIF2B) is a master regulator of protein synthesis under normal and stress conditions. Mutations in any of the five genes encoding its subunits lead to vanishing white matter (VWM) disease, a recessive genetic deadly illness caused by progressive loss of white matter in the brain. In this study we used fibroblasts, which are not involved in the disease, to demonstrate the involvement of eIF2B in mitochondrial function and abundance. Mass spectrometry of total proteome of mouse embryonic fibroblasts (MEFs) isolated from Eif2b5^R132H/R132H mice revealed unbalanced stoichiometry of proteins involved in oxidative phosphorylation and of mitochondrial translation machinery components, among others. Mutant MEFs exhibit 55% decrease in oxygen consumption rate per mtDNA content and 47% increase in mitochondrial abundance (p  3-fold lower ATP-linked respiration per cell despite a 2-fold increase in mtDNA content (p 

Background: The quantitative relations between RNA and protein are fundamental to biology and are still not fully understood. Across taxa, it was demonstrated that the protein-to-mRNA ratio in steady state varies in a direction that lessens the change in protein levels as a result of changes in the transcript abundance. Evidence for this behavior in tissues is sparse. We tested this phenomenon in new data that we produced for the mouse auditory system, and in previously published tissue datasets. A joint analysis of the transcriptome and proteome was performed across four datasets: inner-ear mouse tissues, mouse organ tissues, lymphoblastoid primate samples and human cancer cell lines. Results: We show that the protein levels are more conserved than the mRNA levels in all datasets, and that changes in transcription are associated with translational changes that exert opposite effects on the final protein level, in all tissues except cancer. Finally, we observe that some functions are enriched in the inner ear on the mRNA level but not in protein. Conclusions: We suggest that partial buffering between transcription and translation ensures that proteins can be made rapidly in response to a stimulus. Accounting for the buffering can improve the prediction of protein levels from mRNA levels.

Tumor-associated macrophages (TAMs) promote tumor development, invasion, and dissemination by various mechanisms. In this study, using an orthotopic colorectal cancer (CRC) model, we found that monocyte-derived TAMs advance tumor development by the remodeling of its extracellular matrix (ECM) composition and structure. Unbiased transcriptomic and proteomic analyses of (a) TAM-abundant and -deficient tumor tissues and (b) sorted tumor-associated and -resident colonic macrophage subpopulations defined a distinct TAM-induced ECM molecular signature composed of an ensemble of matricellular proteins and remodeling enzymes they provide to the tumor microenvironment. Remarkably, many of these ECM proteins are specifically increased in human CRC versus healthy colon. Specifically, we demonstrate that although differentiating into TAMs, monocytes up-regulate matrix-remodeling programs associated with the synthesis and assembly of collagenous ECM, specifically collagen types I, VI, and XIV. This finding was further established by advanced imaging showing that TAMs instruct the deposition, cross-linking, and linearization of collagen fibers during tumor development, especially at areas of tumor invasiveness. Finally, we show that cancer-associated fibroblasts are significantly outnumbered by TAMs in this model and that their expression of collagen XIV and I is reduced by TAM deficiency. Here, we outline a novel TAM protumoral function associated with building of the collagenous ECM niche.

A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.

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

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Pelizaeus-Merzbacher disease is an X-linked hypomyelinating leukodystrophy caused by mutations or rearrangements in PLP1. It presents in infancy with nystagmus, jerky head movements, hypotonia and developmental delay evolving into spastic tetraplegia with optic atrophy and variable movement disorders. A clinically similar phenotype caused by recessive mutations in GJC2 is known as Pelizaeus-Merzbacher-like disease. Both genes encode proteins associated with myelin. We describe three siblings of a consanguineous family manifesting the typical infantile-onset Pelizaeus-Merzbacher disease-like phenotype slowly evolving into a form of complicated hereditary spastic paraplegia with mental retardation, dysarthria, optic atrophy and peripheral neuropathy in adulthood. Magnetic resonance imaging and spectroscopy were consistent with a demyelinating leukodystrophy. Using genetic linkage and exome sequencing, we identified a homozygous missense c.399C>G; p.S133R mutation in MAG. This gene, previously associated with hereditary spastic paraplegia, encodes myelin-associated glycoprotein, which is involved in myelin maintenance and glia-axon interaction. This mutation is predicted to destabilize the protein and affect its tertiary structure. Examination of the sural nerve biopsy sample obtained in childhood in the oldest sibling revealed complete absence of myelin-associated glycoprotein accompanied by ill-formed onion-bulb structures and a relatively thin myelin sheath of the affected axons. Immunofluorescence, cell surface labelling, biochemical analysis and mass spectrometry-based proteomics studies in a variety of cell types demonstrated a devastating effect of the mutation on post-translational processing, steady state expression and subcellular localization of myelin-associated glycoprotein. In contrast to the wild-type protein, the p.S133R mutant was retained in the endoplasmic reticulum and was subjected to endoplasmic reticulum-associated protein degradation by the proteasome. Our findings identify involvement of myelin-associated glycoprotein in this family with a disorder affecting the central and peripheral nervous system, and suggest that loss of the protein function is responsible for the unique clinical phenotype.

Vanishing white matter (VWM) is a recessive neurodegenerative disease caused by mutations in translation initiation factor eIF2B and leading to progressive brain myelin deterioration, secondary axonal damage, and death in early adolescence. Eif2b5^R132H/R132H mice exhibit delayed developmental myelination, mild early neurodegeneration and a robust remyelination defect in response to cuprizone-induced demyelination. In the current study we used Eif2b5^R132H/R132H mice for mass-spectrometry analyses, to follow the changes in brain protein abundance in normal- versus cuprizone-diet fed mice during the remyelination recovery phase. Analysis of proteome profiles suggested that dysregulation of mitochondrial functions, altered proteasomal activity and impaired balance between protein synthesis and degradation play a role in VWM pathology. Consistent with these findings, we detected elevated levels of reactive oxygen species in mutant-derived primary fibroblasts and reduced 20S proteasome activity in mutant brain homogenates. These observations highlight the importance of tight translational control to precise coordination of processes involved in myelin formation and regeneration and point at cellular functions that may contribute to VWM pathology. Eif2b5^R132H/R132H mouse model for vanishing white matter (VWM) disease was used for mass spectrometry of brain proteins at two time points under normal conditions and along recovery from cuprizone-induced demyelination. Comparisons of proteome profiles revealed the importance of mitochondrial function and tight coordination between protein synthesis and degradation to myelination formation and regeneration, pointing at cellular functions that contribute to VWM pathology. Eif2b5^R132H/R132H mouse model for vanishing white matter (VWM) disease was used for mass spectrometry of brain proteins at two time points under normal conditions and along recovery from cuprizone-induced demyelination. Comparisons of proteome profiles revealed the importance of mitochondrial function and tight coordination between protein synthesis and degradation to myelination formation and regeneration, pointing at cellular functions that contribute to VWM pathology.

Unbiased proteomic analysis of plasma samples holds the promise to reveal clinically invaluable disease biomarkers. However, the tremendous dynamic range of the plasma proteome has so far hampered the identification of such low abundant markers. To overcome this challenge we analyzed the plasma microparticle proteome, and reached an unprecedented depth of over 3000 plasma proteins in single runs. To add a quantitative dimension, we developed PROMIS-Quan - PROteomics of MIcroparticles with Super-Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) Quantification, a novel mass spectrometry-based technology for plasma microparticle proteome quantification. PROMIS-Quan enables a two-step relative and absolute SILAC quantification. First, plasma microparticle proteomes are quantified relative to a super-SILAC mix composed of cell lines from distinct origins. Next, the absolute amounts of selected proteins of interest are quantified relative to the super-SILAC mix. We applied PROMIS-Quan to prostate cancer and compared plasma microparticle samples of healthy individuals and prostate cancer patients. We identified in total 5374 plasma-microparticle proteins, and revealed a predictive signature of three proteins that were elevated in the patient-derived plasma microparticles. Finally, PROMISQuan enabled determination of the absolute quantitative changes in prostate specific antigen (PSA) upon treatment. We propose PROMIS-Quan as an innovative platform for biomarker discovery, validation, and quantification in both the biomedical research and in the clinical worlds.

Breast tumors are characterized by an extensive desmoplastic stroma, abundantly populated by fibroblasts. Cancer-associated fibroblasts (CAF) support tumorigenesis by stimulating angiogenesis, cancer cell proliferation, and invasion. CAF also orchestrate tumor-promoting inflammation in multiple tumor types, including breast cancer. However, the mechanisms through which normal tissue fibroblasts are reprogrammed to tumor-promoting CAFs are mainly obscure. Here, we show that mammary fibroblasts can be educated by breast cancer cells to become activated to a proinflammatory state that supports malignant progression. Proteomic analysis of breast cancer cell-secreted factors identified the secreted proinflammatory mediator osteopontin, which has been implicated in inflammation, tumor progression, and metastasis. Osteopontin was highly secreted by mouse and human breast cancer cells, and tumor cell-secreted osteopontin activated a CAF phenotypes in normal mammary fibroblasts in vitro and in vivo. Osteopontin was sufficient to induce fibroblast reprogramming and neutralizing antibodies against osteopontin-blocked fibroblast activation induced by tumor cells. The ability of secreted osteopontin to activate mammary fibroblasts relied upon its known receptors CD44 and α_Vβ₃ integrin. Strikingly, osteopontin silencing in tumor cells in vivo attenuated stromal activation and inhibited tumor growth. Our findings establish a critical functional role for paracrine signaling by tumor-derived osteopontin in reprograming normal fibroblasts into tumor-promoting CAFs.

p53 is a pivotal tumor suppressor and a major barrier against cancer. We now report that silencing of the Hippo pathway tumor suppressors LATS1 and LATS2 in nontransformed mammary epithelial cells reduces p53 phosphorylation and increases its association with the p52 NF-κB subunit. Moreover, it partly shifts p53s conformation and transcriptional output toward a state resembling cancer-associated p53 mutants and endows p53 with the ability to promote cell migration. Notably, LATS1 and LATS2 are frequently down-regulated in breast cancer; we propose that such down-regulation might benefit cancer by converting p53 from a tumor suppressor into a tumor facilitator.

Cellular life depends on continuous transport of lipids and small molecules between mitochondria and the endomembrane system. Recently, endoplasmic reticulum-mitochondrial encounter structure(ERMES) was identified as an important yet nonessential contact for such transport. Using a high-contentscreen in yeast, we found a contact site, marked by Vam6/Vps39, between vacuoles (the yeast lysosomal compartment) and mitochondria, named vCLAMP (va. cuo. le and mitochondria patch). vCLAMP is enriched with ion and amino-acid transporters and has a role in lipid relay between the endomembrane system and mitochondria. Critically, we show that mitochondria are dependent on having one of two contact sites, ERMES or vCLAMP. The absence of one causes expansion of the other,and elimination of both is lethal. Identification of vCLAMP adds to our ability to understand the complexity of interorganellar crosstalk.

p53 is a well-known tumor suppressor that is mutated in over 50% of human cancers. These mutations were shown to exhibit gain of oncogenic function compared with the deletion of the gene. Additionally, p53 has fundamental roles in differentiation and development; nevertheless, mutant p53 mice are viable and develop malignant tumors only on adulthood. We set out to reveal the mechanisms by which embryos are protected from mutant p53-induced transformation using ES cells (ESCs) that express a conformational mutant of p53. We found that, despite harboring mutant p53, the ESCs remain pluripotent and benign and have relatively normal karyotype compared with ESCs knocked out for p53. Additionally, using high-content RNA sequencing, we show that p53 is transcriptionally active in response to DNA damage in mutant ESCs and elevates p53 target genes, such as p21 and btg2. We also show that the conformation of mutant p53 protein in ESCs is stabilized to a WT conformation. Through MS-based interactome analyses, we identified a network of proteins, including the CCT complex, USP7, Aurora kinase, Nedd4, and Trim24, that bind mutant p53 and may shift its conformation to a WT form. We propose this conformational shift as a novel mechanism of maintenance of genomic integrity, despite p53 mutation. Harnessing the ability of these protein interactors to transform the oncogenic mutant p53 to the tumor suppressor WT form can be the basis for future development of p53-targeted cancer therapy.

Stable isotope labeling with amino acids in cell culture (SILAC) has risen as a powerful quantification technique in mass spectrometry (MS)-based proteomics in classical and modified forms. Previously, SILAC was limited to cultured cells because of the requirement of active protein synthesis; however, in recent years, it was expanded to model organisms and tissue samples. Specifically, the super-SILAC technique uses a mixture of SILAC-labeled cells as a spike-in standard for accurate quantification of unlabeled samples, thereby enabling quantification of human tissue samples. Here, we highlight the recent developments in super-SILAC and its application to the study of clinical samples, secretomes, post-translational modifications and organelle proteomes. Finally, we propose super-SILAC as a robust and accurate method that can be commercialized and applied to basic and clinical research.

Understanding cell proliferation mechanisms has been a long-lasting goal of the scientific community and specifically of cancer researchers. Previous genome-scale studies of cancer proliferation determinants have mainly relied on knockdown screens aimed to gauge their effects on cancer growth. This powerful approach has several limitations such as off-target effects, partial knockdown, and masking effects due to functional backups. Here we employ a complementary approach and assign each gene a cancer Proliferation Index (cPI) that quantifies the association between its expression levels and growth rate measurements across 60 cancer cell lines. Reassuringly, genes found essential in cancer gene knockdown screens exhibit significant positive cPI values, while tumor suppressors exhibit significant negative cPI values. Cell cycle, DNA replication, splicing and protein production related processes are positively associated with cancer proliferation, while cellular migration is negatively associated with it - in accordance with the well known "go or grow" dichotomy. A parallel analysis of genes' non-cancerous proliferation indices (nPI) across 224 lymphoblastoid cell lines reveals surprisingly marked differences between cancerous and non-cancerous proliferation. These differences highlight genes in the translation and spliceosome machineries as selective cancer proliferation-associated proteins. A cross species comparison reveals that cancer proliferation resembles that of microorganisms while non-cancerous proliferation does not. Furthermore, combining cancerous and non-cancerous proliferation signatures leads to enhanced prediction of patient outcome and gene essentiality in cancer. Overall, these results point to an inherent difference between cancerous and non-cancerous proliferation determinants, whose understanding may contribute to the future development of novel cancer-specific anti-proliferative drugs.

Identifying the building blocks of mammalian tissues is a precondition for understanding their function. In particular, global and quantitative analysis of the proteome of mammalian tissues would point to tissue-specific mechanisms and place the function of each protein in a whole-organism perspective. We performed proteomic analyses of 28 mouse tissues using high-resolution mass spectrometry and used a mix of mouse tissues labeled via stable isotope labeling with amino acids in cell culture as a "spike-in" internal standard for accurate protein quantification across these tissues. We identified a total of 7,349 proteins and quantified 6,974 of them. Bioinformatic data analysis showed that physiologically related tissues clustered together and that highly expressed proteins represented the characteristic tissue functions. Tissue specialization was reflected prominently in the proteomic profiles and is apparent already in their hundred most abundant proteins. The proportion of strictly tissue-specific proteins appeared to be small. However, even proteins with household functions, such as those in ribosomes and spliceosomes, can have dramatic expression differences among tissues. We describe a computational framework with which to correlate proteome profiles with physiological functions of the tissue. Our data will be useful to the broad scientific community as an initial atlas of protein expression of a mammalian species.

Cell biologists studying cell adhesion have already figured out that cell-extracellular matrix connections, mediated by integrin receptors, are diverse and extremely complex structures. Dozens of adaptors-linking integrins with the cytoskeleton, and numerous enzymes and signaling proteins-regulating adhesion site dynamics, collectively referred to as the integrin adhesome, cooperate in mediating adhesion and activating specific signaling networks. Recent proteomic studies indicate that the known adhesome complexity is just the tip of the iceberg. In each existing category of molecular function the number of candidate components more than double the known components and several new categories are suggested. Proteomic analysis of different integrin heterodimers points to integrin-specific variations in composition and analysis of adhesion complexes under varying tension regimes highlights the force-dependent recruitment of different components, most notably LIM domain proteins.

MS-based proteomics generates rapidly increasing amounts of precise and quantitative information. Analysis of individual proteomic experiments has made great strides, but the crucial ability to compare and store information across different proteome measurements still presents many challenges. For example, it has been difficult to avoid contamination of databases with low quality peptide identifications, to control for the inflation in false positive identifications when combining data sets, and to integrate quantitative data. Although, for example, the contamination with low quality identifications has been addressed by joint analysis of deposited raw data in some public repositories, we reasoned that there should be a role for a database specifically designed for high resolution and quantitative data. Here we describe a novel database termed MaxQB that stores and displays collections of large proteomics projects and allows joint analysis and comparison. We demonstrate the analysis tools of MaxQB using proteome data of 11 different human cell lines and 28 mouse tissues. The database-wide false discovery rate is controlled by adjusting the project specific cutoff scores for the combined data sets. The 11 cell line proteomes together identify proteins expressed from more than half of all human genes. For each protein of interest, expression levels estimated by label-free quantification can be visualized across the cell lines. Similarly, the expression rank order and estimated amount of each protein within each proteome are plotted. We used MaxQB to calculate the signal reproducibility of the detected peptides for the same proteins across different proteomes. Spearman rank correlation between peptide intensity and detection probability of identified proteins was greater than 0.8 for 64% of the proteome, whereas a minority of proteins have negative correlation. This information can be used to pinpoint false protein identifications, independently of peptide database scores. The information contained in MaxQB, including high resolution fragment spectra, is accessible to the community via a userfriendly web interface at http://www.biochem.mpg.de/maxqb.

In-depth MS-based proteomics has necessitated fractionation of either proteins or peptides or both, often requiring considerable analysis time. Here we employ long liquid chromatography runs with high resolution coupled to an instrument with fast sequencing speed to investigate how much of the proteome is directly accessible to liquid chromatography-tandem MS characterization without any prefractionation steps. Triplicate single-run analyses identified 2990 yeast proteins, 68% of the total measured in a comprehensive yeast proteome. Among them, we covered the enzymes of the glycolysis and gluconeogenesis pathway targeted in a recent multiple reaction monitoring study. In a mammalian cell line, we identified 5376 proteins in a triplicate run, including representatives of 173 out of 200 KEGG metabolic and signaling pathways. Remarkably, the majority of proteins could be detected in the samples at sub-femtomole amounts and many in the low attomole range, in agreement with absolute abundance estimation done in previous works (Picotti et al. Cell, 138, 795-806, 2009). Our results imply an unexpectedly large dynamic range of the MS signal and sensitivity for liquid chromatography-tandem MS alone. With further development, single-run analysis has the potential to radically simplify many proteomic studies while maintaining a systems-wide view of the proteome.

Along the transformation process, cells accumulate DNA aberrations, including mutations, translocations, amplifications, and deletions. Despite numerous studies, the overall effects of amplifications and deletions on the end point of gene expression-the level of proteins-is generally unknown. Here we use large-scale and high-resolution proteomics combined with gene copy number analysis to investigate in a global manner to what extent these genomic changes have a proteomic output and therefore the ability to affect cellular transformation. We accurately measure expression levels of 6,735 proteins and directly compare them to the gene copy number. We find that the average effect of these alterations on the protein expression is only a few percent. Nevertheless, by using a novel algorithm, we find the combined impact that many of these regional chromosomal aberrations have at the protein level. We show that proteins encoded by amplified oncogenes are often overexpressed, while adjacent amplified genes, which presumably do not promote growth and survival, are attenuated. Furthermore, regulation of biological processes and molecular complexes is independent of general copy number changes. By connecting the primary genome alteration to their proteomic consequences, this approach helps to interpret the data from large-scale cancer genomics efforts.

We describe a method to accurately quantify human tumor proteomes by combining a mixture of five stable-isotope labeling by amino acids in cell culture (SILAC)-labeled cell lines with human carcinoma tissue. This generated hundreds of thousands of isotopically labeled peptides in appropriate amounts to serve as internal standards for mass spectrometry-based analysis. By decoupling the labeling from the measurement, this super-SILAC method broadens the scope of SILAC-based proteomics.