Christoph Schaab

Comparative Proteomic Analysis of Eleven Common Cell Lines Reveals Ubiquitous but Varying Expression of Most Proteins

Deep proteomic analysis of mammalian cell lines would yield an inventory of the building blocks of the most commonly used systems in biological research. Mass spectrometry-based proteomics can identify and quantify proteins in a global and unbiased manner and can highlight the cellular processes that are altered between such systems. We analyzed 11 human cell lines using an LTQ-Orbitrap family mass spectrometer with a “high field” Orbitrap mass analyzer with improved resolution and sequencing speed. We identified a total of 11,731 proteins, and on average 10,361 ± 120 proteins in each cell line. This very high proteome coverage enabled analysis of a broad range of processes and functions. Despite the distinct origins of the cell lines, our quantitative results showed surprisingly high similarity in terms of expressed proteins. Nevertheless, this global similarity of the proteomes did not imply equal expression levels of individual proteins across the 11 cell lines, as we found significant differences in expression levels for an estimated two-third of them. The variability in cellular expression levels was similar for low and high abundance proteins, and even many of the most highly expressed proteins with household roles showed significant differences between cells. Metabolic pathways, which have high redundancy, exhibited variable expression, whereas basic cellular functions such as the basal transcription machinery varied much less. We harness knowledge of these cell line proteomes for the construction of a broad coverage “super-SILAC” quantification standard. Together with the accompanying paper (Schaab, C. MCP 2012, PMID: 22301388) (17) these data can be used to obtain reference expression profiles for proteins of interest both within and across cell line proteomes.

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.

Analysis of High Accuracy, Quantitative Proteomics Data in the MaxQB Database

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 user-friendly web interface at http://www.biochem.mpg.de/maxqb.

Integrated genomics and proteomics define huntingtin CAG length-dependent networks in mice

To gain insight into how mutant huntingtin (mHtt) CAG repeat length modifies Huntingtons disease (HD) pathogenesis, we profiled mRNA in over 600 brain and peripheral tissue samples from HD knock-in mice with increasing CAG repeat lengths. We found repeat length-dependent transcriptional signatures to be prominent in the striatum, less so in cortex, and minimal in the liver. Coexpression network analyses revealed 13 striatal and 5 cortical modules that correlated highly with CAG length and age, and that were preserved in HD models and sometimes in patients. Top striatal modules implicated mHtt CAG length and age in graded impairment in the expression of identity genes for striatal medium spiny neurons and in dysregulation of cyclic AMP signaling, cell death and protocadherin genes. We used proteomics to confirm 790 genes and 5 striatal modules with CAG length–dependent dysregulation at the protein level, and validated 22 striatal module genes as modifiers of mHtt toxicities in vivo.

Time- and compartment-resolved proteome profiling of the extracellular niche in lung injury and repair

The extracellular matrix (ECM) is a key regulator of tissue morphogenesis and repair. However, its composition and architecture are not well characterized. Here, we monitor remodeling of the extracellular niche in tissue repair in the bleomycin‐induced lung injury mouse model. Mass spectrometry quantified 8,366 proteins from total tissue and bronchoalveolar lavage fluid (BALF) over the course of 8 weeks, surveying tissue composition from the onset of inflammation and fibrosis to its full recovery. Combined analysis of proteome, secretome, and transcriptome highlighted post‐transcriptional events during tissue fibrogenesis and defined the composition of airway epithelial lining fluid. To comprehensively characterize the ECM, we developed a quantitative detergent solubility profiling (QDSP) method, which identified Emilin‐2 and collagen‐XXVIII as novel constituents of the provisional repair matrix. QDSP revealed which secreted proteins interact with the ECM, and showed drastically altered association of morphogens to the insoluble matrix upon injury. Thus, our proteomic systems biology study assigns proteins to tissue compartments and uncovers their dynamic regulation upon lung injury and repair, potentially contributing to the development of anti‐fibrotic strategies.

Phosphosignature Predicts Dasatinib Response in Non-small Cell Lung Cancer

Targeted drugs are less toxic than traditional chemotherapeutic therapies; however, the proportion of patients that benefit from these drugs is often smaller. A marker that confidently predicts patient response to a specific therapy would allow an individual therapy selection most likely to benefit the patient. Here, we used quantitative mass spectrometry to globally profile the basal phosphoproteome of a panel of non-small cell lung cancer cell lines. The effect of the kinase inhibitor dasatinib on cellular growth was tested against the same panel. From the phosphoproteome profiles, we identified 58 phosphorylation sites, which consistently differ between sensitive and resistant cell lines. Many of the corresponding proteins are involved in cell adhesion and cytoskeleton organization. We showed that a signature of only 12 phosphorylation sites is sufficient to accurately predict dasatinib sensitivity. Four of the phosphorylation sites belong to integrin β4, a protein that mediates cell-matrix or cell-cell adhesion. The signature was validated in cross-validation and label switch experiments and in six independently profiled breast cancer cell lines. The study supports that the phosphorylation of integrin β4, as well as eight further proteins comprising the signature, are candidate biomarkers for predicting response to dasatinib in solid tumors. Furthermore, our results show that identifying predictive phosphorylation signatures from global, quantitative phosphoproteomic data is possible and can open a new path to discovering molecular markers for response prediction.

Identifying differentially regulated subnetworks from phosphoproteomic data

BackgroundVarious high throughput methods are available for detecting regulations at the level of transcription, translation or posttranslation (e.g. phosphorylation). Integrating these data with protein networks should make it possible to identify subnetworks that are significantly regulated. Furthermore, such integration can support identification of regulated entities from often noisy high throughput data. In particular, processing mass spectrometry-based phosphoproteomic data in this manner may expose signal transduction pathways and, in the case of experiments with drug-treated cells, reveal the drugs mode of action.ResultsHere, we introduce SubExtractor, an algorithm that combines phosphoproteomic data with protein network information from STRING to identify differentially regulated subnetworks and individual proteins. The method is based on a Bayesian probabilistic model combined with a genetic algorithm and rigorous significance testing. The Bayesian model accounts for information about both differential regulation and network topology. The method was tested with artificial data and subsequently applied to a comprehensive phosphoproteomics study investigating the mode of action of sorafenib, a small molecule kinase inhibitor.ConclusionsSubExtractor reliably identifies differentially regulated subnetworks from phosphoproteomic data by integrating protein networks. The method can also be applied to gene or protein expression data.

Comparison of SILAC and mTRAQ quantification for phosphoproteomics on a quadrupole orbitrap mass spectrometer.

Advances in mass spectrometric methodology and instrumentation have promoted a continuous increase in analytical performance in the field of phosphoproteomics. Here, we employed the recently introduced quadrupole Orbitrap (Q Exactive) mass spectrometer for quantitative signaling analysis to a depth of more than 15 000 phosphorylation sites. In parallel to the commonly used SILAC approach, we evaluated the nonisobaric chemical labeling reagent mTRAQ as an alternative quantification technique. Both enabled high phosphoproteome coverage in H3122 lung cancer cells. Replicate quantifications by mTRAQ identified almost as many significant phosphorylation changes upon treatment with ALK kinase inhibitor crizotinib as found by SILAC quantification. Overall, mTRAQ was slightly less precise than SILAC as evident from a somewhat higher variance of replicate phosphosite ratios. Direct comparison of SILAC- and mTRAQ-quantified phosphosites revealed that the majority of changes were detected by either quantification techniques, but also highlighted the aspect of false negative identifications in quantitative proteomics applications. Further inspection of crizotinib-regulated phosphorylation changes unveiled interference with multiple antioncogenic mechanisms downstream of ALK fusion kinase in H3122 cells. In conclusion, our results demonstrate a strong analytical performance of the Q Exactive in global phosphoproteomics, and establish mTRAQ quantification as a useful alternative to metabolic isotope labeling.

Global Quantitative Phosphoproteome Analysis of Human Tumor Xenografts Treated with a CD44 Antagonist

The cell surface glycoprotein CD44 plays an important role in the development and progression of various tumor types. RG7356 is a humanized antibody targeting the constant region of CD44 that shows antitumor efficacy in mice implanted with CD44-expressing tumors such as MDA-MB-231 breast cancer cells. CD44 receptor seems to function as the main receptor for hyaluronic acid and osteopontin, serving as coreceptor for growth factor pathways like cMet, EGFR, HER-2, and VEGFR and by cytoskeletal modulation via ERM and Rho kinase signaling. To assess the direct impact of RG7356 binding to the CD44 receptor, a global mass spectrometry-based phosphoproteomics approach was applied to freshly isolated MDA-MB-231 tumor xenografts. Results from a global phosphoproteomics screen were further corroborated by Western blot and ELISA analyses of tumor lysates from CD44-expressing tumors. Short-term treatment of tumor-bearing mice with RG7356 resulted in modifications of the MAPK pathway in the responsive model, although no effects on downstream phosphorylation were observed in a nonresponsive xenograft model. Taken together, our approach augments the value of other high throughput techniques to identify biomarkers for clinical development of targeted agents.

A SILAC-based Approach Identifies Substrates of Caspase-dependent Cleavage upon TRAIL-induced Apoptosis

The extracellular ligand-induced extrinsic pathway of apoptosis is executed via caspase protease cascades that activate downstream effectors by means of site-directed proteolysis. Here we identify proteome changes upon the induction of apoptosis by the cytokine tumor necrosis factor–related apoptosis-inducing ligand (TRAIL) in a Jurkat T cell line. We detected caspase-dependent cleavage substrates by quantifying protein intensities before and after TRAIL induction in SDS gel slices. Apoptotic protein cleavage events are identified by a characteristic stable isotope labeling with amino acids in cell culture (SILAC) ratio pattern across gel slices that results from differential migration of the cleaved and uncleaved proteins. We applied a statistical test to define apoptotic substrates in the proteome. Our approach identified more than 650 of these cleaved proteins in response to TRAIL-induced apoptosis, including many previously unknown substrates and cleavage sites. Inhibitor treatment combined with triple SILAC demonstrated that the detected cleavage events were caspase dependent. Proteins located in the lumina of organelles such as mitochondria and endoplasmic reticulum were significantly underrepresented in the substrate population. Interestingly, caspase cleavage is generally observed in not only one but several members of stable complexes, but often with lower stoichiometry. For instance, all five proteins of the condensin I complex were cleaved upon TRAIL treatment. The apoptotic substrate proteome data can be accessed and visualized in the MaxQB database and might prove useful for basic and clinical research into TRAIL-induced apoptosis. The technology described here is extensible to a wide range of other proteolytic cleavage events.