Extracellular Heparan 6-O-Endosulfatases SULF1 and SULF2 in Head and Neck Squamous Cell Carcinoma and Other Malignancies.

Pan-cancer analysis of TCGA and CPTAC (proteomics) data shows that SULF1 and SULF2 are oncogenic in a number of human malignancies and associated with poor survival outcomes. Our studies document a consistent upregulation of SULF1 and SULF2 in HNSC which is associated with poor survival outcomes. These heparan sulfate editing enzymes were considered largely functional redundant but single-cell RNAseq (scRNAseq) shows that SULF1 is secreted by cancer-associated fibroblasts in contrast to the SULF2 derived from tumor cells. Our RNAScope and patient-derived xenograft (PDX) analysis of the HNSC tissues fully confirm the stromal source of SULF1 and explain the uniform impact of this enzyme on the biology of multiple malignancies. In summary, SULF2 expression increases in multiple malignancies but less consistently than SULF1, which uniformly increases in the tumor tissues and negatively impacts survival in several types of cancer even though its expression in cancer cells is low. This paradigm is common to multiple malignancies and suggests a potential for diagnostic and therapeutic targeting of the heparan sulfatases in cancer diseases.

Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification.

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep-learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT, and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson's correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep-learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods.

Ubiquitin Conjugation Probed by Inflammation in Myeloid-Derived Suppressor Cell Extracellular Vesicles.

Ubiquitinated proteins carried by the extracellular vesicles (EV) released by myeloid-derived suppressor cells (MDSC) have been investigated using proteomic strategies to examine the effect of tumor-associated inflammation. EV were collected from MDSC directly following isolation from tumor-bearing mice with low and high inflammation. Among the 1092 proteins (high inflammation) and 925 proteins (low inflammation) identified, more than 50% were observed as ubiquitinated proteoforms. More than three ubiquitin-attachment sites were characterized per ubiquitinated protein, on average. Multiple ubiquitination sites were identified in the pro-inflammatory proteins S100 A8 and S100 A9, characteristic of MDSC and in histones and transcription regulators among other proteins. Spectral counting and pathway analysis suggest that ubiquitination occurs independently of inflammation. Some ubiquitinated proteins were shown to cause the migration of MDSC, which has been previously connected with immune suppression and tumor progression. Finally, MDSC EV are found collectively to carry all the enzymes required to catalyze ubiquitination, and the hypothesis is presented that a portion of the ubiquitinated proteins are produced in situ.

Differential Content of Proteins, mRNAs, and miRNAs Suggests that MDSC and Their Exosomes May Mediate Distinct Immune Suppressive Functions.

Myeloid-derived suppressor cells (MDSC) are immature myeloid cells that accumulate in the circulation and the tumor microenvironment of most cancer patients. There, MDSC suppress both adaptive and innate immunity, hindering immunotherapies. The inflammatory milieu often present in cancers facilitates MDSC suppressive activity, causing aggressive tumor progression and metastasis. MDSC from tumor-bearing mice release exosomes, which carry biologically active proteins and mediate some of the immunosuppressive functions characteristic of MDSC. Studies on other cell types have shown that exosomes may also carry RNAs which can be transferred to local and distant cells, yet the mRNA and microRNA cargo of MDSC-derived exosomes has not been studied to date. Here, the cargo of MDSC and their exosomes was interrogated with the goal of identifying and characterizing molecules that may facilitate MDSC suppressive potency. Because inflammation is an established driving force for MDSC suppressive activity, we used the well-established 4T1 mouse mammary carcinoma system, which includes "conventional" as well as "inflammatory" MDSC. We provide evidence that MDSC-derived exosomes carry proteins, mRNAs, and microRNAs with different quantitative profiles than those of their parental cells. Several of these molecules have known or predicted functions consistent with MDSC suppressive activity, suggesting a potential mechanistic redundancy.

Protein Identification from Tandem Mass Spectra by Database Searching.

Protein identification from tandem mass spectra is one of the most versatile and widely used proteomics workflows, able to identify proteins, characterize post-translational modifications, and provide semiquantitative measurements of relative protein abundance. This manuscript describes the concepts, prerequisites, and methods required to analyze a tandem mass spectrometry dataset in order to identify its proteins, by using a tandem mass spectrometry search engine to search protein sequence databases. The discussion includes instructions for extraction, preparation, and formatting of spectral datafiles, selection of appropriate search parameter settings, and basic interpretation of the results.

Site-specific analysis of changes in the glycosylation of proteins in liver cirrhosis using data-independent workflow with soft fragmentation.

Cirrhosis of the liver is associated with increased fucosylation of proteins in the plasma. We describe a data-independent (DIA) strategy for comparative analysis of the site-specific glycoforms of plasma glycoproteins. A library of 161 glycoforms of 25 N-glycopeptides was established by data-dependent LC-MS/MS analysis of a tryptic digest of 14 human protein groups retained on a multiple affinity removal column. The collision-induced dissociation conditions were adjusted to maximize the yield of selective Y-ions which were quantified by a data-independent mass spectrometry workflow using a 10-Da acquisition window. Using this workflow, we quantified 125 glycoforms of 25 glycopeptides, covering 10 of the 14 proteins, without any further glycopeptide enrichment. Comparison of the proteins in the plasma of healthy controls and cirrhotic patients shows an average 1.5-fold increase in the fucosylation of bi-antennary glycoforms and 3-fold increase in the fucosylation of tri- and tetra- antennary glycoforms. These results show that the adjusted glycopeptide DIA workflow using soft collision-induced fragmentation of glycopeptides is suitable for site-specific analysis of protein glycosylation in complex mixtures of analytes without glycopeptide enrichment.

Evaluation of Spectral Counting for Relative Quantitation of Proteoforms in Top-Down Proteomics.

Spectral counting is a straightforward label-free quantitation strategy used in bottom-up proteomics workflows. The application of spectral counting in label-free top-down proteomics workflows can be similarly straightforward but has not been applied as widely as quantitation by chromatographic peak areas or peak intensities. In this study, we evaluate spectral counting for quantitative comparisons in label-free top-down proteomics workflows by comparison with chromatographic peak areas and intensities. We tested these quantitation approaches by spiking standard proteins into a complex protein background and comparing relative quantitation by spectral counts with normalized chromatographic peak areas and peak intensities from deconvoluted extracted ion chromatograms of the spiked proteins. Ratio estimates and statistical significance of differential abundance from each quantitation technique are evaluated against the expected ratios and each other. In this experiment, spectral counting was able to detect differential abundance of spiked proteins for expected ratios ≥2, with comparable or higher sensitivity than normalized areas and intensities. We also found that while ratio estimates using peak areas and intensities are usually more accurate, the spectral-counting-based estimates are not substantially worse. Following the evaluation and comparison of these label-free top-down quantitation strategies using spiked proteins, spectral counting, along with normalized chromatographic peak areas and intensities, were used to analyze the complex protein cargo of exosomes shed by myeloid-derived suppressor cells collected under high and low conditions of inflammation, revealing statistically significant differences in abundance for several proteoforms, including the active pro-inflammatory proteins S100A8 and S100A9.

Peptide-based systems analysis of inflammation induced myeloid-derived suppressor cells reveals diverse signaling pathways.

A better understanding of molecular signaling between myeloid-derived suppressor cells (MDSC), tumor cells, T-cells, and inflammatory mediators is expected to contribute to more effective cancer immunotherapies. We focus on plasma membrane associated proteins, which are critical in signaling and intercellular communication, and investigate changes in their abundance in MDSC of tumor-bearing mice subject to heightened versus basal inflammatory conditions. Using spectral counting, we observed statistically significant differential abundances for 35 proteins associated with the plasma membrane, most notably the pro-inflammatory proteins S100A8 and S100A9 which induce MDSC and promote their migration. We also tested whether the peptides associated with canonical pathways showed a statistically significant increase or decrease subject to heightened versus basal inflammatory conditions. Collectively, these studies used bottom-up proteomic analysis to identify plasma membrane associated pro-inflammatory molecules and pathways that drive MDSC accumulation, migration, and suppressive potency.

A Description of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) Common Data Analysis Pipeline.

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has produced large proteomics data sets from the mass spectrometric interrogation of tumor samples previously analyzed by The Cancer Genome Atlas (TCGA) program. The availability of the genomic and proteomic data is enabling proteogenomic study for both reference (i.e., contained in major sequence databases) and nonreference markers of cancer. The CPTAC laboratories have focused on colon, breast, and ovarian tissues in the first round of analyses; spectra from these data sets were produced from 2D liquid chromatography-tandem mass spectrometry analyses and represent deep coverage. To reduce the variability introduced by disparate data analysis platforms (e.g., software packages, versions, parameters, sequence databases, etc.), the CPTAC Common Data Analysis Platform (CDAP) was created. The CDAP produces both peptide-spectrum-match (PSM) reports and gene-level reports. The pipeline processes raw mass spectrometry data according to the following: (1) peak-picking and quantitative data extraction, (2) database searching, (3) gene-based protein parsimony, and (4) false-discovery rate-based filtering. The pipeline also produces localization scores for the phosphopeptide enrichment studies using the PhosphoRS program. Quantitative information for each of the data sets is specific to the sample processing, with PSM and protein reports containing the spectrum-level or gene-level ("rolled-up") precursor peak areas and spectral counts for label-free or reporter ion log-ratios for 4plex iTRAQ. The reports are available in simple tab-delimited formats and, for the PSM-reports, in mzIdentML. The goal of the CDAP is to provide standard, uniform reports for all of the CPTAC data to enable comparisons between different samples and cancer types as well as across the major omics fields.

The CPTAC Data Portal: A Resource for Cancer Proteomics Research.

The Clinical Proteomic Tumor Analysis Consortium (CPTAC), under the auspices of the National Cancer Institute's Office of Cancer Clinical Proteomics Research, is a comprehensive and coordinated effort to accelerate the understanding of the molecular basis of cancer through the application of proteomic technologies and workflows to clinical tumor samples with characterized genomic and transcript profiles. The consortium analyzes cancer biospecimens using mass spectrometry, identifying and quantifying the constituent proteins and characterizing each tumor sample's proteome. Mass spectrometry enables highly specific identification of proteins and their isoforms, accurate relative quantitation of protein abundance in contrasting biospecimens, and localization of post-translational protein modifications, such as phosphorylation, on a protein's sequence. The combination of proteomics, transcriptomics, and genomics data from the same clinical tumor samples provides an unprecedented opportunity for tumor proteogenomics. The CPTAC Data Portal is the centralized data repository for the dissemination of proteomic data collected by Proteome Characterization Centers (PCCs) in the consortium. The portal currently hosts 6.3 TB of data and includes proteomic investigations of breast, colorectal, and ovarian tumor tissues from The Cancer Genome Atlas (TCGA). The data collected by the consortium is made freely available to the public through the data portal.

SNPlice: variants that modulate Intron retention from RNA-sequencing data.

RATIONALE: The growing recognition of the importance of splicing, together with rapidly accumulating RNA-sequencing data, demand robust high-throughput approaches, which efficiently analyze experimentally derived whole-transcriptome splice profiles. RESULTS: We have developed a computational approach, called SNPlice, for identifying cis-acting, splice-modulating variants from RNA-seq datasets. SNPlice mines RNA-seq datasets to find reads that span single-nucleotide variant (SNV) loci and nearby splice junctions, assessing the co-occurrence of variants and molecules that remain unspliced at nearby exon-intron boundaries. Hence, SNPlice highlights variants preferentially occurring on intron-containing molecules, possibly resulting from altered splicing. To illustrate co-occurrence of variant nucleotide and exon-intron boundary, allele-specific sequencing was used. SNPlice results are generally consistent with splice-prediction tools, but also indicate splice-modulating elements missed by other algorithms. SNPlice can be applied to identify variants that correlate with unexpected splicing events, and to measure the splice-modulating potential of canonical splice-site SNVs. AVAILABILITY AND IMPLEMENTATION: SNPlice is freely available for download from https://code.google.com/p/snplice/ as a self-contained binary package for 64-bit Linux computers and as python source-code. CONTACT: pmudvari@gwu.edu or horvatha@gwu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ubiquitinated proteins in exosomes secreted by myeloid-derived suppressor cells.

We provide evidence at the molecular level that ubiquitinated proteins are present in exosomes shed by myeloid-derived suppressor cells (MDSC). Ubiquitin was selected as a post-translational modification of interest because it is known to play a determinant role in the endosomal trafficking that culminates in exosome release. Enrichment was achieved by two immunoprecipitations, first at the protein level and subsequently at the peptide level. Fifty ubiquitinated proteins were identified by tandem mass spectrometry filtering at a 5% spectral false discovery rate and using the conservative requirement that glycinylglycine-modified lysine residues were observed in tryptic peptides. Thirty five of these proteins have not previously been reported to be ubiquitinated. The ubiquitinated cohort spans a range of protein sizes and favors basic pI values and hydrophobicity. Five proteins associated with endosomal trafficking were identified as ubiquitinated, along with pro-inflammatory high mobility group protein B1 and proinflammatory histones.

Site-specific glycan microheterogeneity of inter-alpha-trypsin inhibitor heavy chain H4.

Inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4) is a 120 kDa acute-phase glycoprotein produced primarily in the liver, secreted into the blood, and identified in serum. ITIH4 is involved in liver development and stabilization of the extracellular matrix (ECM), and its expression is altered in liver disease. In this study, we aimed to characterize glycosylation of recombinant and serum-derived ITIH4 using analytical mass spectrometry. Recombinant ITIH4 was analyzed to optimize glycopeptide analyses, followed by serum-derived ITIH4. First, we confirmed that the four ITIH4 N-X-S/T sequons (N81, N207, N517, and N577) were glycosylated by treating ITIH4 tryptic/GluC glycopeptides with PNGaseF in the presence of (18)O water. Next, we performed glycosidase-assisted LC-MS/MS analysis of ITIH4 trypsin-GluC glycopeptides enriched via hydrophilic interaction liquid chromatography to characterize ITIH4 N-glycoforms. While microheterogeneity of N-glycoforms differed between ITIH4 protein expressed in HEK293 cells and protein isolated from serum, occupancy of N-glycosylation sites did not differ. A fifth N-glycosylation site was discovered at N274 with the rare nonconsensus NVV motif. Site N274 contained high-mannose N-linked glycans in both serum and recombinant ITIH4. We also identified isoform-specific ITIH4 O-glycoforms and documented that utilization of O-glycosylation sites on ITIH4 differed between the cell line and serum.

A peptide-spectrum scoring system based on ion alignment, intensity, and pair probabilities.

Peppy, the proteogenomic/proteomic search software, employs a novel method for assessing the match quality between an MS/MS spectrum and a theorized peptide sequence. The scoring system uses three score factors calculated with binomial probabilities: the probability that a fragment ion will randomly align with a peptide ion, the probability that the aligning ions will be selected from subsets of the most intense peaks, and the probability that the intensities of fragment ions identified as y-ions are greater than those of their counterpart b-ions. The scores produced by the method act as global confidence scores, which facilitate the accurate comparison of results and the estimation of false discovery rates. Peppy has been integrated into the meta-search engine PepArML to produce meaningful comparisons with Mascot, MSGF+, OMSSA, X!Tandem, k-Score and s-Score. For two of the four data sets examined with the PepArML analysis, Peppy exceeded the accuracy performance of the other scoring systems. Peppy is available for download at http://geneffects.com/peppy .

The human secretome atlas initiative: implications in health and disease conditions.

Proteomic analysis of human body fluids is highly challenging, therefore many researchers are redirecting efforts toward secretome profiling. The goal is to define potential biomarkers and therapeutic targets in the secretome that can be traced back in accessible human body fluids. However, currently there is a lack of secretome profiles of normal human primary cells making it difficult to assess the biological meaning of current findings. In this study we sought to establish secretome profiles of human primary cells obtained from healthy donors with the goal of building a human secretome atlas. Such an atlas can be used as a reference for discovery of potential disease associated biomarkers and eventually novel therapeutic targets. As a preliminary study, secretome profiles were established for six different types of human primary cell cultures and checked for overlaps with the three major human body fluids including plasma, cerebrospinal fluid and urine. About 67% of the 1054 identified proteins in the secretome of these primary cells occurred in at least one body fluid. Furthermore, comparison of the secretome profiles of two human glioblastoma cell lines to this new human secretome atlas enabled unambiguous identification of potential brain tumor biomarkers. These biomarkers can be easily monitored in different body fluids using stable isotope labeled standard proteins. The long term goal of this study is to establish a comprehensive online human secretome atlas for future use as a reference for any disease related secretome study. This article is part of a Special Issue entitled: An Updated Secretome.

Mass-biased partitioning to enhance middle down proteomics analysis.

A strategy is presented for enhancing the middle-down analysis of higher mass peptides recovered from complex protein mixtures. Following a 30-min digestion of multiple myeloma cell lysate by an acid cleavage reaction that is selective for aspartic acid, a 3000 Da membrane filter is used to bifurcate the peptide product mixture, and the heavier fraction is subjected to collisional activation with precursor selection that excludes charge states below +4. Filtration and charge state selection are shown to provide significant increases in the number of peptides identified in the mass range above 3000 Da and in information about protein sequences.

Enrichment of plasma membrane proteins using nanoparticle pellicles: comparison between silica and higher density nanoparticles.

Proteomic and other characterization of plasma membrane proteins is made difficult by their low abundance, hydrophobicity, frequent carboxylation, and dynamic population. We and others have proposed that underrepresentation in LC-MS/MS analysis can be partially compensated by enriching the plasma membrane and its proteins using cationic nanoparticle pellicles. The nanoparticles increase the density of plasma membrane sheets and thus enhance separation by centrifugation from other lysed cellular components. Herein, we test the hypothesis that the use of nanoparticles with increased densities can provide enhanced enrichment of plasma membrane proteins for proteomic analysis. Multiple myeloma cells were grown and coated in suspension with three different pellicles of three different densities and both pellicle coated and uncoated suspensions analyzed by high-throughput LC-MS/MS. Enrichment was evaluated by the total number and the spectral counts of identified plasma membrane proteins.

A bioinformatics approach for integrated transcriptomic and proteomic comparative analyses of model and non-sequenced anopheline vectors of human malaria parasites.

Malaria morbidity and mortality caused by both Plasmodium falciparum and Plasmodium vivax extend well beyond the African continent, and although P. vivax causes between 80 and 300 million severe cases each year, vivax transmission remains poorly understood. Plasmodium parasites are transmitted by Anopheles mosquitoes, and the critical site of interaction between parasite and host is at the mosquito's luminal midgut brush border. Although the genome of the "model" African P. falciparum vector, Anopheles gambiae, has been sequenced, evolutionary divergence limits its utility as a reference across anophelines, especially non-sequenced P. vivax vectors such as Anopheles albimanus. Clearly, technologies and platforms that bridge this substantial scientific gap are required in order to provide public health scientists with key transcriptomic and proteomic information that could spur the development of novel interventions to combat this disease. To our knowledge, no approaches have been published that address this issue. To bolster our understanding of P. vivax-An. albimanus midgut interactions, we developed an integrated bioinformatic-hybrid RNA-Seq-LC-MS/MS approach involving An. albimanus transcriptome (15,764 contigs) and luminal midgut subproteome (9,445 proteins) assembly, which, when used with our custom Diptera protein database (685,078 sequences), facilitated a comparative proteomic analysis of the midgut brush borders of two important malaria vectors, An. gambiae and An. albimanus.

PepArML: A Meta-Search Peptide Identification Platform for Tandem Mass Spectra.

The PepArML meta-search peptide identification platform for tandem mass spectra provides a unified search interface to seven search engines; a robust cluster, grid, and cloud computing scheduler for large-scale searches; and an unsupervised, model-free, machine-learning-based result combiner, which selects the best peptide identification for each spectrum, estimates false-discovery rates, and outputs pepXML format identifications. The meta-search platform supports Mascot; Tandem with native, k-score and s-score scoring; OMSSA; MyriMatch; and InsPecT with MS-GF spectral probability scores­reformatting spectral data and constructing search configurations for each search engine on the fly. The combiner selects the best peptide identification for each spectrum based on search engine results and features that model enzymatic digestion, retention time, precursor isotope clusters, mass accuracy, and proteotypic peptide properties, requiring no prior knowledge of feature utility or weighting. The PepArML meta-search peptide identification platform often identifies two to three times more spectra than individual search engines at 10% FDR.