Proteomic profiles of human lung adeno and squamous cell carcinoma using super‐SILAC and label‐free quantification approaches

Nonsmall cell lung cancer (NSCLC) accounts for 85% of lung cancers, and is subdivided into two major histological subtypes: adenocarcinoma (ADC) and squamous cell carcinoma (SCC). There is an unmet need to further subdivide NSCLC according to distinctive molecular features that may be associated with responsiveness to therapies. Four primary tumor‐derived xenograft proteomes (two‐each ADC and SCC) were quantitatively compared by using a super‐SILAC labeling approach together with ultrahigh‐resolution MS. Proteins highly differentially expressed in the two subtypes were identified, including 30 that were validated in an independent cohort of 12 NSCLC primary tumor‐derived xenograft tumors whose proteomes were quantified by an alternative, label‐free shotgun MS methodology. The 30‐protein signature contains metabolism enzymes including phosphoglycerate dehydrogenase, which is more highly expressed in SCC, as well as a comprehensive set of cytokeratins and other components of the epithelial barrier, which is therefore distinctly different between ADC and SCC. These results demonstrate the utility of the super‐SILAC method for the characterization of primary tissues, and compatibility with datasets derived from different MS‐based platforms. The validation of proteome signatures of NSCLC subtypes supports the further development and application of MS‐based quantitative proteomics as a basis for precision classifications and treatments of tumors. All MS data have been deposited in the ProteomeXchange with identifier PXD000438 ( http://proteomecentral.proteomexchange.org/dataset/PXD000438 ).     

Proteomic investigation of the secretome of Cellvibrio japonicus during growth on chitin

Studies of the secretomes of microbes grown on insoluble substrates are important for the discovery of novel proteins involved in biomass conversion. However, data in literature and this study indicate that secretome samples tend to be contaminated with cytoplasmic proteins. We have examined the secretome of the Gram‐negative soil bacterium Cellvibrio japonicus using a simple plate‐based culturing technique that yields samples with high fractions (60–75%) of proteins that are predicted to be secreted. By combining this approach with label‐free quantification using the MaxLFQ algorithm, we have mapped and quantified proteins secreted by C. japonicus during growth on α‐ and β‐chitin. Hierarchical clustering of the detected protein quantities revealed groups of up‐regulated proteins that include all five putative C. japonicus chitinases as well as a chitin‐specific lytic polysaccharide monooxygenase (CjLPMO10A). A small set of secreted proteins were co‐regulated with known chitin‐specific enzymes, including several with unknown catalytic functions. These proteins provide interesting targets for further studies aimed at unraveling the enzymatic machineries used by C. japonicus for recalcitrant polysaccharide degradation. Studies of chitin degradation indicated that C. japonicus indeed produces an efficient chitinolytic enzyme cocktail. All MS data have been deposited in the ProteomeXchange with the dataset identifier PXD002843 ( http://proteomecentral.proteomexchange.org/dataset/PXD002843 ).     

Qualitative and quantitative analysis of the adult Drosophila melanogaster proteome

Drosophila melanogaster is one of the most widely used model organisms in life sciences. Mapping its proteome is of great significance for understanding the biological characteristics and tissue functions of this species. However, the comprehensive coverage of its proteome remains a challenge. Here, we describe a high‐coverage analysis of whole fly through a 1D gel electrophoresis and LC‐MS/MS approach. By combining the datasets of two types of SDS‐PAGE and two kinds of tagmata, the high‐coverage analysis resulted in the identification of 5262 genes, which correspond to 38.23% of the entire coding genes. Moreover, we found that the fly head and body have different molecular weight distributions of their proteomes when the proteins were resolved with SDS‐PAGE and image analysis of the stained gel. This phenomenon was further confirmed by both label‐free and isobaric tags for relative and absolute quantitation‐based quantitative approaches. The consistent results of the two different quantitation methods also demonstrated the stability and accuracy of the LC‐MS/MS platform. The MS proteomics data have been deposited to the ProteomeXchange with identifiers PXD000454 and PXD000455 ( http://proteomecentral.proteomexchange.org/dataset/PXD000454 ; ( http://proteomecentral.proteomexchange.org/dataset/PXD000455 ).     

Identification of inositol 1,4,5‐trisphosphate‐binding proteins by heparin‐agarose affinity purification and LTQ ORBITRAP MS in Oryza sativa

Inositol 1,4,5‐trisphosohate (IP₃) and its receptors play a pivotal role in calcium signal transduction in mammals. However, no homologs of mammalian IP₃ receptors have been found in plants. In this study, we isolated the microsomal fractions from rice cells in suspension culture and further obtained putative IP₃‐binding proteins by heparin‐agarose affinity purification. The IP₃‐binding activities of these protein fractions were determined by [³H] IP₃‐binding assay. SDS‐PAGE and MS analysis were then performed to characterize these proteins. We have identified 297 proteins from the eluates of heparin‐agarose column chromatography, which will provide insight into the IP₃ signaling pathways in plants. All MS data have been deposited in the ProteomeXchange with identifier PXD000763 ( http://proteomecentral.proteomexchange.org/dataset/PXD000763 ).     

In‐depth protein profiling of the postsynaptic density from mouse hippocampus using data‐independent acquisition proteomics

Located at neuronal terminals, the postsynaptic density (PSD) is a highly complex network of cytoskeletal scaffolding and signaling proteins responsible for the transduction and modulation of glutamatergic signaling between neurons. Using ion‐mobility enhanced data‐independent label‐free LC‐MS/MS, we established a reference proteome of crude synaptosomes, synaptic junctions, and PSD derived from mouse hippocampus including TOP3‐based absolute quantification values for identified proteins. The final dataset across all fractions comprised 49 491 peptides corresponding to 4558 protein groups. Of these, 2102 protein groups were identified in highly purified PSD in at least two biological replicates. Identified proteins play pivotal roles in neurological and synaptic processes providing a rich resource for studies on hippocampal PSD function as well as on the pathogenesis of neuropsychiatric disorders. All MS data have been deposited in the ProteomeXchange with identifier PXD000590 ( http://proteomecentral.proteomexchange.org/dataset/PXD000590 ).     

Characterization of individual mouse cerebrospinal fluid proteomes

Analysis of cerebrospinal fluid (CSF) offers key insight into the status of the CNS. Characterization of murine CSF proteomes can provide a valuable resource for studying CNS injury and disease in animal models. However, the small volume of CSF in mice has thus far limited individual mouse proteome characterization. Through nonterminal CSF extractions in C57Bl/6 mice and high‐resolution 2D‐LC MS/MS analysis of individual murine samples, we report the most comprehensive proteome characterization of individual murine CSF to date. We identified a total of 566 unique proteins, including 128 proteins from three individual CSF samples that have been previously identified in brain tissue. Our methods and analysis provide a mechanism for individual murine CSF proteome analysis. The data are available in the ProteomeXchange with identifier PXD000248 ( http://proteomecentral.proteomexchange.org/dataset/PXD000248 ).     

OLFM4 Expression in Ductal Carcinoma In Situ and in Invasive Breast Cancer Cohorts by a SWATH‐Based Proteomic Approach

Human olfactomedin‐4 (OLFM4) is a secreted protein involved in a variety of cellular functions including proliferation, differentiation, apoptosis, and cell adhesion. OLFM4 expression has been studied in several tumor types including gastric, colorectal, lung, and endometrioid cancers where it has been suggested to be an independent favorable or unfavorable prognostic marker. For breast cancer, the clinical significance of OLFM4 is still unclear. In the present study, SWATH‐MS is used as a tool for the robust identification and quantification of breast tissue proteins. SWATH‐MS data show that OLFM4 expression is higher in DCIS than in invasive breast cancer. In‐depth analysis of the breast tumor proteome show that OLFM4 is a favorable pronostic marker. Serum OLFM4 levels in peripheral blood are also analyzed by ELISA in 825 cases, including 94 cases of healthy individuals, 61 cases of non‐invasive breast tumor (DCIS) and 670 cases of breast cancer (BC). It is found that serum OLFM4 levels are significantly higher in the DCIS cohort and in the breast cancer cohort compared with the healthy controls. This result suggests that circulating OLFM4 could be an interesting biomarker of early breast cancer. Data are available via ProteomeXchange with identifier PXD014194.     

LC‐MS/MS‐based serum proteomics for identification of candidate biomarkers for hepatocellular carcinoma

Associating changes in protein levels with the onset of cancer has been widely investigated to identify clinically relevant diagnostic biomarkers. In the present study, we analyzed sera from 205 patients recruited in the United States and Egypt for biomarker discovery using label‐free proteomic analysis by LC‐MS/MS. We performed untargeted proteomic analysis of sera to identify candidate proteins with statistically significant differences between hepatocellular carcinoma (HCC) and patients with liver cirrhosis. We further evaluated the significance of 101 proteins in sera from the same 205 patients through targeted quantitation by MRM on a triple quadrupole mass spectrometer. This led to the identification of 21 candidate protein biomarkers that were significantly altered in both the United States and Egyptian cohorts. Among the 21 candidates, ten were previously reported as HCC‐associated proteins (eight exhibiting consistent trends with our observation), whereas 11 are new candidates discovered by this study. Pathway analysis based on the significant proteins reveals upregulation of the complement and coagulation cascades pathway and downregulation of the antigen processing and presentation pathway in HCC cases versus patients with liver cirrhosis. The results of this study demonstrate the power of combining untargeted and targeted quantitation methods for a comprehensive serum proteomic analysis, to evaluate changes in protein levels and discover novel diagnostic biomarkers. All MS data have been deposited in the ProteomeXchange with identifier PXD001171 ( http://proteomecentral.proteomexchange.org/dataset/PXD001171 ).     

Absolute protein quantification allows differentiation of cell‐specific metabolic routes and functions

Total protein approach (TPA) is a proteomic method that allows calculation of concentrations of individual proteins and groups of functionally related proteins in any protein mixture without spike‐in standards. Using the two‐step digestion–filter‐aided sample preparation method and LC‐MS/MS analysis, we generated comprehensive quantitative datasets of mouse intestinal mucosa, liver, red muscle fibers, brain, and of human plasma, erythrocytes, and tumor cells lines. We show that the TPA‐based quantitative data reflect well‐defined and specific physiological functions of different organs and cells, for example nutrient absorption and transport in intestine, amino acid catabolism and bile secretion in liver, and contraction of muscle fibers. Focusing on key metabolic processes, we compared metabolic capacities in different tissues and cells. In addition, we demonstrate quantitative differences in the mitochondrial proteomes. Providing insight into the abundances of mitochondrial metabolite transporters, we demonstrate that their titers are well tuned to cell‐specific metabolic requirements. This study provides for the first time a comprehensive overview of the protein hardware mediating metabolism in different mammalian organs and cells. The presented approach can be applied to any other system to study biological processes. All MS data have been deposited in the ProteomeXchange with identifier PXD001352 ( http://proteomecentral.proteomexchange.org/dataset/PXD001352 ).     

Proteomic analysis of extracellular vesicles derived from Mycobacterium tuberculosis

The release of extracellular vesicles, also known as outer membrane vesicles, membrane vesicles, exosomes, and microvesicles, is an evolutionarily conserved phenomenon from bacteria to eukaryotes. It has been reported that Mycobacterium tuberculosis releases extracellular vesicles harboring immunologically active molecules, and these extracellular vesicles have been suggested to be applicable in vaccine development and biomarker discovery. However, the comprehensive proteomic analysis has not been performed for M. tuberculosis extracellular vesicles. In this study, we identified a total of 287 vesicular proteins by four LC‐MS/MS analyses with high confidence. In addition, we identified several vesicular proteins associated with the virulence of M. tuberculosis. This comprehensive proteome profile will help elucidate the pathogenic mechanism of M. tuberculosis. The data have been deposited to the ProteomeXchange with identifier PXD001160 ( http://proteomecentral.proteomexchange.org/dataset/PXD001160 ).     

EBprot: Statistical analysis of labeling‐based quantitative proteomics data

Labeling‐based proteomics is a powerful method for detection of differentially expressed proteins (DEPs). The current data analysis platform typically relies on protein‐level ratios, which is obtained by summarizing peptide‐level ratios for each protein. In shotgun proteomics, however, some proteins are quantified with more peptides than others, and this reproducibility information is not incorporated into the differential expression (DE) analysis. Here, we propose a novel probabilistic framework EBprot that directly models the peptide‐protein hierarchy and rewards the proteins with reproducible evidence of DE over multiple peptides. To evaluate its performance with known DE states, we conducted a simulation study to show that the peptide‐level analysis of EBprot provides better receiver‐operating characteristic and more accurate estimation of the false discovery rates than the methods based on protein‐level ratios. We also demonstrate superior classification performance of peptide‐level EBprot analysis in a spike‐in dataset. To illustrate the wide applicability of EBprot in different experimental designs, we applied EBprot to a dataset for lung cancer subtype analysis with biological replicates and another dataset for time course phosphoproteome analysis of EGF‐stimulated HeLa cells with multiplexed labeling. Through these examples, we show that the peptide‐level analysis of EBprot is a robust alternative to the existing statistical methods for the DE analysis of labeling‐based quantitative datasets. The software suite is freely available on the Sourceforge website http://ebprot.sourceforge.net/ . All MS data have been deposited in the ProteomeXchange with identifier PXD001426 ( http://proteomecentral.proteomexchange.org/dataset/PXD001426/ ).     

Loss of neprilysin alters protein expression in the brain of Alzheimer's disease model mice

Alzheimer's disease (AD) is a neurodegenerative disease displaying extracellular plaques formed by the neurotoxic amyloid β‐peptide (Aβ), and intracellular neurofibrillary tangles consisting of protein tau. However, how these pathologies relate to the massive neuronal death that occurs in AD brains remain elusive. Neprilysin is the major Aβ‐degrading enzyme and a lack thereof increases Aβ levels in the brain twofold. To identify altered protein expression levels induced by increased Aβ levels, we performed a proteomic analysis of the brain of the AD mouse model APPsw and compared it to that of APPsw mice lacking neprilysin. To this end we established an LC‐MS/MS method to analyze brain homogenate, using an ¹⁸O‐labeled internal standard to accurately quantify the protein levels. To distinguish between alterations in protein levels caused by increased Aβ levels and those induced by neprilysin deficiency independently of Aβ, the brain proteome of neprilysin deficient APPsw mice was also compared to that of neprilysin deficient mice. By this approach we identified approximately 600 proteins and the levels of 300 of these were quantified. Pathway analysis showed that many of the proteins with altered expression were involved in neurological disorders, and that tau, presenilin and APP were key regulators in the identified networks. The data have been deposited to the ProteomeXchange Consortium with identifiers PXD000968 and PXD001786 ( http://proteomecentral.proteomexchange.org/dataset/PXD000968 and ( http://proteomecentral.proteomexchange.org/dataset/PXD001786 ). Interestingly, the levels of several proteins, including some not previously reported to be linked to AD, were associated with increased Aβ levels.     

Calibration plot for proteomics: A graphical tool to visually check the assumptions underlying FDR control in quantitative experiments

In MS‐based quantitative proteomics, the FDR control (i.e. the limitation of the number of proteins that are wrongly claimed as differentially abundant between several conditions) is a major postanalysis step. It is classically achieved thanks to a specific statistical procedure that computes the adjusted p‐values of the putative differentially abundant proteins. Unfortunately, such adjustment is conservative only if the p‐values are well‐calibrated; the false discovery control being spuriously underestimated otherwise. However, well‐calibration is a property that can be violated in some practical cases. To overcome this limitation, we propose a graphical method to straightforwardly and visually assess the p‐value well‐calibration, as well as the R codes to embed it in any pipeline. All MS data have been deposited in the ProteomeXchange with identifier PXD002370 ( http://proteomecentral.proteomexchange.org/dataset/PXD002370 ).     

Comparative interactomes of SIRT6 and SIRT7: Implication of functional links to aging

Sirtuins are NAD⁺‐dependent deacetylases that regulate a range of cellular processes. Although diverse functions of sirtuins have been proposed, those functions of SIRT6 and SIRT7 that are mediated by their interacting proteins remain elusive. In the present study, we identified SIRT6‐ and SIRT7‐interacting proteins, and compared their interactomes to investigate functional links. Our interactomes revealed 136 interacting proteins for SIRT6 and 233 for SIRT7 while confirming seven and 111 proteins identified previously for SIRT6 and SIRT7, respectively. Comparison of SIRT6 and SIRT7 interactomes under the same experimental conditions disclosed 111 shared proteins, implying related functional links. The interaction networks of interactomes indicated biological processes associated with DNA repair, chromatin assembly, and aging. Interactions of two highly acetylated proteins, nucleophosmin (NPM1) and nucleolin, with SIRT6 and SIRT7 were confirmed by co‐immunoprecipitation. NPM1 was found to be deacetylated by both SIRT6 and SIRT7. In senescent cells, the acetylation level of NPM1 was increased in conjunction with decreased levels of SIRT6 and SIRT7, suggesting that the acetylation of NPM1 could be regulated by SIRT6 and SIRT7 in the aging process. Our comparative interactomic study of SIRT6 and SIRT7 implies important functional links to aging by their associations with interacting proteins. All MS data have been deposited in the ProteomeXchange with identifiers PXD000159 and PXD000850 ( http://proteomecentral.proteomexchange.org/dataset/PXD000159 , http://proteomecentral.proteomexchange.org/dataset/PXD000850 ).     

Spectral Libraries for SWATH‐MS Assays for Drosophila melanogaster and Solanum lycopersicum

Quantitative proteomics methods have emerged as powerful tools for measuring protein expression changes at the proteome level. Using MS‐based approaches, it is now possible to routinely quantify thousands of proteins. However, prefractionation of the samples at the protein or peptide level is usually necessary to go deep into the proteome, increasing both MS analysis time and technical variability. Recently, a new MS acquisition method named SWATH is introduced with the potential to provide good coverage of the proteome as well as a good measurement precision without prior sample fractionation. In contrast to shotgun‐based MS however, a library containing experimental acquired spectra is necessary for the bioinformatics analysis of SWATH data. In this study, spectral libraries for two widely used models are built to study crop ripening or animal embryogenesis, Solanum lycopersicum (tomato) and Drosophila melanogaster, respectively. The spectral libraries comprise fragments for 5197 and 6040 proteins for S. lycopersicum and D. melanogaster, respectively, and allow reproducible quantification for thousands of peptides per MS analysis. The spectral libraries and all MS data are available in the MassIVE repository with the dataset identifiers MSV000081074 and MSV000081075 and the PRIDE repository with the dataset identifiers PXD006493 and PXD006495.     

Distinct serum proteome profiles associated with collagen‐induced arthritis and complete Freund's adjuvant‐induced inflammation in CD38−/− mice: The discriminative power of protein species or proteoforms

Collagen‐type‐II‐induced arthritis (CIA) is an autoimmune disease, which involves a complex host systemic response including inflammatory and autoimmune reactions. CIA is milder in CD38^?/? than in wild‐type (WT) mice. ProteoMiner‐equalized serum samples were subjected to 2D‐DiGE and MS‐MALDI‐TOF/TOF analyses to identify proteins that changed in their relative abundances in CD38^?/? versus WT mice either with arthritis (CIA⁺), with no arthritis (CIA^?), or with inflammation (complete Freund's adjuvant (CFA)‐treated mice). Multivariate analyses revealed that a multiprotein signature (n = 28) was able to discriminate CIA⁺ from CIA^? mice, and WT from CD38^?/? mice within each condition. Likewise, a distinct multiprotein signature (n = 16) was identified which differentiated CIA⁺ CD38^?/? mice from CIA⁺ WT mice, and lastly, a third multiprotein signature (n = 18) indicated that CD38^?/? and WT mice could be segregated in response to CFA treatment. Further analyses showed that the discriminative power to distinguish these groups was reached at protein species level and not at the protein level. Hence, the need to identify and quantify proteins at protein species level to better correlate proteome changes with disease processes. It is crucial for plasma proteomics at the low‐abundance protein species level to apply the ProteoMiner enrichment. All MS data have been deposited in the ProteomeXchange with identifiers PXD001788, PXD001799 and PXD002071 ( http://proteomecentral.proteomexchange.org/dataset/PXD001788 , http://proteomecentral.proteomexchange.org/dataset/PXD001799 and http://proteomecentral.proteomexchange.org/dataset/PXD002071 ).     

Proteomic analysis of N‐glycosylation of human seminal plasma

Seminal plasma is a mixture of secretions from several male accessory glands. The seminal plasma contains many secreted proteins which are important for sperm function and male fertility. In this study, we employed N‐linked glycosylated peptide enrichment, combined with LC–MS/MS analysis, and establish the first large scale N‐linked glycoproteome of human seminal plasma. Combined with the results of five biological replicates, a total of 720 N‐glycosylated sites on 372 proteins were identified. Analysis of variations among five individuals revealed similar compositions of N‐glycosylated proteins in seminal plasma. The N‐linked glycoproteome could help us understanding the biological functions of human seminal plasma. The data set could also be a resource for further screening of biomarkers for male diseases including cancer and infertility at the level of N‐glycosylation. For example, N‐glycosylated prostate‐specific antigen is known to be an efficient biomarker that can distinguish benign prostate hyperplasia from prostate cancer. All MS data have been deposited in the ProteomeXchange with identifier PXD000959 ( http://proteomecentral.proteomexchange.org/dataset/PXD000959 ).