Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma.

To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules. To assess the degree of immune infiltration in individual tumors, we identified microenvironment cell signatures that delineated four immune-based ccRCC subtypes characterized by distinct cellular pathways. This study reports a large-scale proteogenomic analysis of ccRCC to discern the functional impact of genomic alterations and provides evidence for rational treatment selection stemming from ccRCC pathobiology.

Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis.

Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometrybased glycoproteomics datasets from human serum were shared with participants and the relative team performance for N- and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved 'high-coverage' and 'high-accuracy' glycoproteomics search solutions. This study concludes that diverse software packages for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies and specifies key variables that will guide future software developments and assist informatics decision-making in glycoproteomics.

SCExecute: custom cell barcode-stratified analyses of scRNA-seq data.

MOTIVATION: In single-cell RNA-sequencing (scRNA-seq) data, stratification of sequencing reads by cellular barcode is necessary to study cell-specific features. However, apart from gene expression, the analyses of cell-specific features are not sufficiently supported by available tools designed for high-throughput sequencing data. RESULTS: We introduce SCExecute, which executes a user-provided command on barcode-stratified, extracted on-the-fly, single-cell binary alignment map (scBAM) files. SCExecute extracts the alignments with each cell barcode from aligned, pooled single-cell sequencing data. Simple commands, monolithic programs, multi-command shell scripts or complex shell-based pipelines are then executed on each scBAM file. scBAM files can be restricted to specific barcodes and/or genomic regions of interest. We demonstrate SCExecute with two popular variant callers-GATK and Strelka2-executed in shell-scripts together with commands for BAM file manipulation and variant filtering, to detect single-cell-specific expressed single nucleotide variants from droplet scRNA-seq data (10X Genomics Chromium System).In conclusion, SCExecute facilitates custom cell-level analyses on barcoded scRNA-seq data using currently available tools and provides an effective solution for studying low (cellular) frequency transcriptome features. AVAILABILITY AND IMPLEMENTATION: SCExecute is implemented in Python3 using the Pysam package and distributed for Linux, MacOS and Python environments from https://horvathlab.github.io/NGS/SCExecute. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Glycan Structure Dictionary-a dictionary describing commonly used glycan structure terms.

Recent technological advances in glycobiology have resulted in a large influx of data and the publication of many papers describing discoveries in glycoscience. However, the terms used in describing glycan structural features are not standardized, making it difficult to harmonize data across biomolecular databases, hampering the harvesting of information across studies and hindering text mining and curation efforts. To address this shortcoming, the Glycan Structure Dictionary has been developed as a reference dictionary to provide a standardized list of widely used glycan terms that can help in the curation and mapping of glycan structures described in publications. Currently, the dictionary has 190 glycan structure terms with 297 synonyms linked to 3,332 publications. For a term to be included in the dictionary, it must be present in at least 2 peer-reviewed publications. Synonyms, annotations, and cross-references to GlyTouCan, GlycoMotif, and other relevant databases and resources are also provided when available. The purpose of this effort is to facilitate biocuration, assist in the development of text mining tools, improve the harmonization of search, and browse capabilities in glycoinformatics resources and help to map glycan structures to function and disease. It is also expected that authors will use these terms to describe glycan structures in their manuscripts over time. A mechanism is also provided for researchers to submit terms for potential incorporation. The dictionary is available at https://wiki.glygen.org/Glycan_structure_dictionary.

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.

Enhancing the interoperability of glycan data flow between ChEBI, PubChem and GlyGen.

Glycans play a vital role in health, disease, bioenergy, biomaterials and bio-therapeutics. As a result, there is keen interest to identify and increase glycan data in bioinformatics databases like ChEBI and PubChem, and connecting them to resources at the EMBL-EBI and NCBI to facilitate access to important annotations at a global level. GlyTouCan is a comprehensive archival database that contains glycans obtained primarily through batch upload from glycan repositories, glycoprotein databases and individual laboratories. In many instances, the glycan structures deposited in GlyTouCan may not be fully defined or have supporting experimental evidence and citations. Databases like ChEBI and PubChem were designed to accommodate complete atomistic structures with well-defined chemical linkages. As a result, they cannot easily accommodate the structural ambiguity inherent in glycan databases. Consequently, there is a need to improve the organization of glycan data coherently to enhance connectivity across the major NCBI, EMBL-EBI and glycoscience databases. This paper outlines a workflow developed in collaboration between GlyGen, ChEBI and PubChem to improve the visibility and connectivity of glycan data across these resources. GlyGen hosts a subset of glycans (~29,000) from the GlyTouCan database and has submitted valuable glycan annotations to the PubChem database and integrated over 10,500 (including ambiguously defined) glycans into the ChEBI database. The integrated glycans were prioritized based on links to PubChem and connectivity to glycoprotein data. The pipeline provides a blueprint for how glycan data can be harmonized between different resources. The current PubChem, ChEBI and GlyTouCan mappings can be downloaded from GlyGen (https://data.glygen.org).

The glycoconjugate ontology (GlycoCoO) for standardizing the annotation of glycoconjugate data and its application.

Recent years have seen great advances in the development of glycoproteomics protocols and methods resulting in a sustainable increase in the reporting proteins, their attached glycans and glycosylation sites. However, only very few of these reports find their way into databases or data repositories. One of the major reasons is the absence of digital standard to represent glycoproteins and the challenging annotations with glycans. Depending on the experimental method, such a standard must be able to represent glycans as complete structures or as compositions, store not just single glycans but also represent glycoforms on a specific glycosylation side, deal with partially missing site information if no site mapping was performed, and store abundances or ratios of glycans within a glycoform of a specific site. To support the above, we have developed the GlycoConjugate Ontology (GlycoCoO) as a standard semantic framework to describe and represent glycoproteomics data. GlycoCoO can be used to represent glycoproteomics data in triplestores and can serve as a basis for data exchange formats. The ontology, database providers and supporting documentation are available online (https://github.com/glycoinfo/GlycoCoO).

SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data.

BACKGROUND: Recent studies have demonstrated the utility of scRNA-seq SNVs to distinguish tumor from normal cells, characterize intra-tumoral heterogeneity, and define mutation-associated expression signatures. In addition to cancer studies, SNVs from single cells have been useful in studies of transcriptional burst kinetics, allelic expression, chromosome X inactivation, ploidy estimations, and haplotype inference. RESULTS: To aid these types of studies, we have developed a tool, SCReadCounts, for cell-level tabulation of the sequencing read counts bearing SNV reference and variant alleles from barcoded scRNA-seq alignments. Provided genomic loci and expected alleles, SCReadCounts generates cell-SNV matrices with the absolute variant- and reference-harboring read counts, as well as cell-SNV matrices of expressed Variant Allele Fraction (VAFRNA) suitable for a variety of downstream applications. We demonstrate three different SCReadCounts applications on 59,884 cells from seven neuroblastoma samples: (1) estimation of cell-level expression of known somatic mutations and RNA-editing sites, (2) estimation of cell- level allele expression of biallelic SNVs, and (3) a discovery mode assessment of the reference and each of the three alternative nucleotides at genomic positions of interest that does not require prior SNV information. For the later, we applied SCReadCounts on the coding regions of KRAS, where it identified known and novel somatic mutations in a low-to-moderate proportion of cells. The SCReadCounts read counts module is benchmarked against the analogous modules of GATK and Samtools. SCReadCounts is freely available ( https://github.com/HorvathLab/NGS ) as 64-bit self-contained binary distributions for Linux and MacOS, in addition to Python source. CONCLUSIONS: SCReadCounts supplies a fast and efficient solution for estimation of cell-level SNV expression from scRNA-seq data. SCReadCounts enables distinguishing cells with monoallelic reference expression from those with no gene expression and is applicable to assess SNVs present in only a small proportion of the cells, such as somatic mutations in cancer.

GlyGen data model and processing workflow.

SUMMARY: Glycoinformatics plays a major role in glycobiology research, and the development of a comprehensive glycoinformatics knowledgebase is critical. This application note describes the GlyGen data model, processing workflow and the data access interfaces featuring programmatic use case example queries based on specific biological questions. The GlyGen project is a data integration, harmonization and dissemination project for carbohydrate and glycoconjugate-related data retrieved from multiple international data sources including UniProtKB, GlyTouCan, UniCarbKB and other key resources. AVAILABILITY AND IMPLEMENTATION: GlyGen web portal is freely available to access at https://glygen.org. The data portal, web services, SPARQL endpoint and GitHub repository are also freely available at https://data.glygen.org, https://api.glygen.org, https://sparql.glygen.org and https://github.com/glygener, respectively. All code is released under license GNU General Public License version 3 (GNU GPLv3) and is available on GitHub https://github.com/glygener. The datasets are made available under Creative Commons Attribution 4.0 International (CC BY 4.0) license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

RNA2DNAlign: nucleotide resolution allele asymmetries through quantitative assessment of RNA and DNA paired sequencing data.

We introduce RNA2DNAlign, a computational framework for quantitative assessment of allele counts across paired RNA and DNA sequencing datasets. RNA2DNAlign is based on quantitation of the relative abundance of variant and reference read counts, followed by binomial tests for genotype and allelic status at SNV positions between compatible sequences. RNA2DNAlign detects positions with differential allele distribution, suggesting asymmetries due to regulatory/structural events. Based on the type of asymmetry, RNA2DNAlign outlines positions likely to be implicated in RNA editing, allele-specific expression or loss, somatic mutagenesis or loss-of-heterozygosity (the first three also in a tumor-specific setting). We applied RNA2DNAlign on 360 matching normal and tumor exomes and transcriptomes from 90 breast cancer patients from TCGA. Under high-confidence settings, RNA2DNAlign identified 2038 distinct SNV sites associated with one of the aforementioned asymetries, the majority of which have not been linked to functionality before. The performance assessment shows very high specificity and sensitivity, due to the corroboration of signals across multiple matching datasets. RNA2DNAlign is freely available from http://github.com/HorvathLab/NGS as a self-contained binary package for 64-bit Linux systems.

Surface Glycoproteins of Exosomes Shed by Myeloid-Derived Suppressor Cells Contribute to Function.

In this report, we use a proteomic strategy to identify glycoproteins on the surface of exosomes derived from myeloid-derived suppressor cells (MDSCs), and then test if selected glycoproteins contribute to exosome-mediated chemotaxis and migration of MDSCs. We report successful modification of a surface chemistry method for use with exosomes and identify 21 surface N-glycoproteins on exosomes released by mouse mammary carcinoma-induced MDSCs. These glycoprotein identities and functionalities are compared with 93 N-linked glycoproteins identified on the surface of the parental cells. As with the lysate proteomes examined previously, the exosome surface N-glycoproteins are primarily a subset of the glycoproteins on the surface of the suppressor cells that released them, with related functions and related potential as therapeutic targets. The "don't eat me" molecule CD47 and its binding partners thrombospondin-1 (TSP1) and signal regulatory protein α (SIRPα) were among the surface N-glycoproteins detected. Functional bioassays using antibodies to these three molecules demonstrated that CD47, TSP1, and to a lesser extent SIRPα facilitate exosome-mediated MDSC chemotaxis and migration.

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.

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.

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.