Unraveling the glycosylated immunopeptidome with HLA-Glyco.

Recent interest in targeted therapies has been sparked by the study of MHC-associated peptides (MAPs) that undergo post-translational modifications (PTMs), particularly glycosylation. In this study, we introduce a fast computational workflow that merges the MSFragger-Glyco search algorithm with a false discovery rate control for glycopeptide analysis from mass spectrometry-based immunopeptidome data. By analyzing eight large-scale publicly available studies, we find that glycosylated MAPs are predominantly presented by MHC class II. Here, we present HLA-Glyco, a comprehensive resource containing over 3,400 human leukocyte antigen (HLA) class II N-glycopeptides from 1,049 distinct protein glycosylation sites. This resource provides valuable insights, including high levels of truncated glycans, conserved HLA-binding cores, and differences in glycosylation positional specificity between HLA allele groups. We integrate the workflow within the FragPipe computational platform and provide HLA-Glyco as a free web resource. Overall, our work provides a valuable tool and resource to aid the nascent field of glyco-immunopeptidomics.

The Immunopeptidome from a Genomic Perspective: Establishing the Noncanonical Landscape of MHC Class I-Associated Peptides.

Tumor antigens can emerge through multiple mechanisms, including translation of noncoding genomic regions. This noncanonical category of tumor antigens has recently gained attention; however, our understanding of how they recur within and between cancer types is still in its infancy. Therefore, we developed a proteogenomic pipeline based on deep learning de novo mass spectrometry (MS) to enable the discovery of noncanonical MHC class I-associated peptides (ncMAP) from noncoding regions. Considering that the emergence of tumor antigens can also involve posttranslational modifications (PTM), we included an open search component in our pipeline. Leveraging the wealth of MS-based immunopeptidomics, we analyzed data from 26 MHC class I immunopeptidomic studies across 11 different cancer types. We validated the de novo identified ncMAPs, along with the most abundant PTMs, using spectral matching and controlled their FDR to 1%. The noncanonical presentation appeared to be 5 times enriched for the A03 HLA supertype, with a projected population coverage of 55%. The data reveal an atlas of 8,601 ncMAPs with varying levels of cancer selectivity and suggest 17 cancer-selective ncMAPs as attractive therapeutic targets according to a stringent cutoff. In summary, the combination of the open-source pipeline and the atlas of ncMAPs reported herein could facilitate the identification and screening of ncMAPs as targets for T-cell therapies or vaccine development.

Kinomics platform using GBM tissue identifies BTK as being associated with higher patient survival.

Better understanding of GBM signalling networks in-vivo would help develop more physiologically relevant ex vivo models to support therapeutic discovery. A "functional proteomics" screen was undertaken to measure the specific activity of a set of protein kinases in a two-step cell-free biochemical assay to define dominant kinase activities to identify potentially novel drug targets that may have been overlooked in studies interrogating GBM-derived cell lines. A dominant kinase activity derived from the tumour tissue, but not patient-derived GBM stem-like cell lines, was Bruton tyrosine kinase (BTK). We demonstrate that BTK is expressed in more than one cell type within GBM tissue; SOX2-positive cells, CD163-positive cells, CD68-positive cells, and an unidentified cell population which is SOX2-negative CD163-negative and/or CD68-negative. The data provide a strategy to better mimic GBM tissue ex vivo by reconstituting more physiologically heterogeneous cell co-culture models including BTK-positive/negative cancer and immune cells. These data also have implications for the design and/or interpretation of emerging clinical trials using BTK inhibitors because BTK expression within GBM tissue was linked to longer patient survival.

Interfaces with Structure Dynamics of the Workhorses from Cells Revealed through Cross-Linking Mass Spectrometry (CLMS).

The fundamentals of how protein-protein/RNA/DNA interactions influence the structures and functions of the workhorses from the cells have been well documented in the 20th century. A diverse set of methods exist to determine such interactions between different components, particularly, the mass spectrometry (MS) methods, with its advanced instrumentation, has become a significant approach to analyze a diverse range of biomolecules, as well as bring insights to their biomolecular processes. This review highlights the principal role of chemistry in MS-based structural proteomics approaches, with a particular focus on the chemical cross-linking of protein-protein/DNA/RNA complexes. In addition, we discuss different methods to prepare the cross-linked samples for MS analysis and tools to identify cross-linked peptides. Cross-linking mass spectrometry (CLMS) holds promise to identify interaction sites in larger and more complex biological systems. The typical CLMS workflow allows for the measurement of the proximity in three-dimensional space of amino acids, identifying proteins in direct contact with DNA or RNA, and it provides information on the folds of proteins as well as their topology in the complexes. Principal CLMS applications, its notable successes, as well as common pipelines that bridge proteomics, molecular biology, structural systems biology, and interactomics are outlined.

MicroPOTS Analysis of Barrett's Esophageal Cell Line Models Identifies Proteomic Changes after Physiologic and Radiation Stress.

Moving from macroscale preparative systems in proteomics to micro- and nanotechnologies offers researchers the ability to deeply profile smaller numbers of cells that are more likely to be encountered in clinical settings. Herein a recently developed microscale proteomic method, microdroplet processing in one pot for trace samples (microPOTS), was employed to identify proteomic changes in ∼200 Barrett's esophageal cells following physiologic and radiation stress exposure. From this small population of cells, microPOTS confidently identified >1500 protein groups, and achieved a high reproducibility with a Pearson's correlation coefficient value of R > 0.9 and over 50% protein overlap from replicates. A Barrett's cell line model treated with either lithocholic acid (LCA) or X-ray had 21 (e.g., ASNS, RALY, FAM120A, UBE2M, IDH1, ESD) and 32 (e.g., GLUL, CALU, SH3BGRL3, S100A9, FKBP3, AGR2) overexpressed proteins, respectively, compared to the untreated set. These results demonstrate the ability of microPOTS to routinely identify and quantify differentially expressed proteins from limited numbers of cells.

The Proteogenomic Landscape of Curable Prostate Cancer.

DNA sequencing has identified recurrent mutations that drive the aggressiveness of prostate cancers. Surprisingly, the influence of genomic, epigenomic, and transcriptomic dysregulation on the tumor proteome remains poorly understood. We profiled the genomes, epigenomes, transcriptomes, and proteomes of 76 localized, intermediate-risk prostate cancers. We discovered that the genomic subtypes of prostate cancer converge on five proteomic subtypes, with distinct clinical trajectories. ETS fusions, the most common alteration in prostate tumors, affect different genes and pathways in the proteome and transcriptome. Globally, mRNA abundance changes explain only ∼10% of protein abundance variability. As a result, prognostic biomarkers combining genomic or epigenomic features with proteomic ones significantly outperform biomarkers comprised of a single data type.

Detecting protein variants by mass spectrometry: a comprehensive study in cancer cell-lines.

BACKGROUND: Onco-proteogenomics aims to understand how changes in a cancer's genome influences its proteome. One challenge in integrating these molecular data is the identification of aberrant protein products from mass-spectrometry (MS) datasets, as traditional proteomic analyses only identify proteins from a reference sequence database. METHODS: We established proteomic workflows to detect peptide variants within MS datasets. We used a combination of publicly available population variants (dbSNP and UniProt) and somatic variations in cancer (COSMIC) along with sample-specific genomic and transcriptomic data to examine proteome variation within and across 59 cancer cell-lines. RESULTS: We developed a set of recommendations for the detection of variants using three search algorithms, a split target-decoy approach for FDR estimation, and multiple post-search filters. We examined 7.3 million unique variant tryptic peptides not found within any reference proteome and identified 4771 mutations corresponding to somatic and germline deviations from reference proteomes in 2200 genes among the NCI60 cell-line proteomes. CONCLUSIONS: We discuss in detail the technical and computational challenges in identifying variant peptides by MS and show that uncovering these variants allows the identification of druggable mutations within important cancer genes.

Aberrant DNA methylation reprogramming during induced pluripotent stem cell generation is dependent on the choice of reprogramming factors.

The conversion of somatic cells into pluripotent stem cells via overexpression of reprogramming factors involves epigenetic remodeling. DNA methylation at a significant proportion of CpG sites in induced pluripotent stem cells (iPSCs) differs from that of embryonic stem cells (ESCs). Whether different sets of reprogramming factors influence the type and extent of aberrant DNA methylation in iPSCs differently remains unknown. In order to help resolve this critical question, we generated human iPSCs from a common fibroblast cell source using either the Yamanaka factors (OCT4, SOX2, KLF4 and cMYC) or the Thomson factors (OCT4, SOX2, NANOG and LIN28), and determined their genome-wide DNA methylation profiles. In addition to shared DNA methylation aberrations present in all our iPSCs, we identified Yamanaka-iPSC (Y-iPSC)-specific and Thomson-iPSC (T-iPSC)-specific recurrent aberrations. Strikingly, not only were the genomic locations of the aberrations different but also their types: reprogramming with Yamanaka factors mainly resulted in failure to demethylate CpGs, whereas reprogramming with Thomson factors mainly resulted in failure to methylate CpGs. Differences in the level of transcripts encoding DNMT3b and TET3 between Y-iPSCs and T-iPSCs may contribute partially to the distinct types of aberrations. Finally, de novo aberrantly methylated genes in Y-iPSCs were enriched for NANOG targets that are also aberrantly methylated in some cancers. Our study thus reveals that the choice of reprogramming factors influences the amount, location, and class of DNA methylation aberrations in iPSCs. These findings may provide clues into how to produce human iPSCs with fewer DNA methylation abnormalities.

Onco-proteogenomics: cancer proteomics joins forces with genomics.

The complexities of tumor genomes are rapidly being uncovered, but how they are regulated into functional proteomes remains poorly understood. Standard proteomics workflows use databases of known proteins, but these databases do not capture the uniqueness of the cancer transcriptome, with its point mutations, unusual splice variants and gene fusions. Onco-proteogenomics integrates mass spectrometry-generated data with genomic information to identify tumor-specific peptides. Linking tumor-derived DNA, RNA and protein measurements into a central-dogma perspective has the potential to improve our understanding of cancer biology.

pH-induced conformational changes in human ABO(H) blood group glycosyltransferases confirm the importance of electrostatic interactions in the formation of the semi-closed state.

The homologous human ABO(H) A and B blood group glycosyltransferases GTA and GTB have two mobile polypeptide loops surrounding their active sites that serve to allow substrate access and product egress and to recognize and sequester substrates for catalysis. Previous studies have established that these enzymes can move from the "open" state to the "semi-closed" then "closed" states in response to addition of a substrate. The contribution of electrostatic interactions to these conformational changes has now been demonstrated by the determination at various pH of the structures of GTA, GTB and the chimeric enzyme ABBA. At near-neutral pH, GTA displays the closed state in which both mobile loops order around the active site, whereas ABBA and GTB display the open state. At low pH, the apparent protonation of the DXD motif in GTA leads to the expulsion of the donor analog to yield the open state, whereas at high pH, both ABBA and GTB form the semi-closed state in which the first mobile loop becomes an ordered α-helix. Step-wise deprotonation of GTB in increments of 0.5 between pH 6.5 and 10.0 shows that helix ordering is gradual, which indicates that the formation of the semi-closed state is dependent on electrostatic forces consistent with the binding of substrate. Spectropolarimetric studies of the corresponding stand-alone peptide in solution reveal no tendency toward helix formation from pH 7.0 to 10.0, which shows that pH-dependent stability is a product of the larger protein environment and underlines the importance of substrate in active site ordering.

Sequence-dependent effects of cryoprotectants on the active sites of the human ABO(H) blood group A and B glycosyltransferases.

The human ABO(H) A and B blood group glycosyltransferases GTA and GTB differ by only four amino acids, yet this small dissimilarity is responsible for significant differences in biosynthesis, kinetics and structure. Like other glycosyltransferases, these two enzymes have been shown to recognize substrates through dramatic conformational changes in mobile polypeptide loops surrounding the active site. Structures of GTA, GTB and several chimeras determined by single-crystal X-ray diffraction demonstrate a range of susceptibility to the choice of cryoprotectant, in which the mobile polypeptide loops can be induced by glycerol to form the ordered closed conformation associated with substrate recognition and by MPD [hexylene glycol, (±)-2-methyl-2,4-pentanediol] to hinder binding of substrate in the active site owing to chelation of the Mn²âº cofactor and thereby adopt the disordered open state. Glycerol is often avoided as a cryoprotectant when determining the structures of carbohydrate-active enzymes as it may act as a competitive inhibitor for monosaccharide ligands. Here, it is shown that the use of glycerol as a cryoprotectant can additionally induce significant changes in secondary structure, a phenomenon that could apply to any class of protein.

ABO(H) blood group A and B glycosyltransferases recognize substrate via specific conformational changes.

The final step in the enzymatic synthesis of the ABO(H) blood group A and B antigens is catalyzed by two closely related glycosyltransferases, an alpha-(1-->3)-N-acetylgalactosaminyltransferase (GTA) and an alpha-(1-->3)-galactosyltransferase (GTB). Of their 354 amino acid residues, GTA and GTB differ by only four "critical" residues. High resolution structures for GTB and the GTA/GTB chimeric enzymes GTB/G176R and GTB/G176R/G235S bound to a panel of donor and acceptor analog substrates reveal "open," "semi-closed," and "closed" conformations as the enzymes go from the unliganded to the liganded states. In the open form the internal polypeptide loop (amino acid residues 177-195) adjacent to the active site in the unliganded or H antigen-bound enzymes is composed of two alpha-helices spanning Arg(180)-Met(186) and Arg(188)-Asp(194), respectively. The semi-closed and closed forms of the enzymes are generated by binding of UDP or of UDP and H antigen analogs, respectively, and show that these helices merge to form a single distorted helical structure with alternating alpha-3(10)-alpha character that partially occludes the active site. The closed form is distinguished from the semi-closed form by the ordering of the final nine C-terminal residues through the formation of hydrogen bonds to both UDP and H antigen analogs. The semi-closed forms for various mutants generally show significantly more disorder than the open forms, whereas the closed forms display little or no disorder depending strongly on the identity of residue 176. Finally, the use of synthetic analogs reveals how H antigen acceptor binding can be critical in stabilizing the closed conformation. These structures demonstrate a delicately balanced substrate recognition mechanism and give insight on critical aspects of donor and acceptor specificity, on the order of substrate binding, and on the requirements for catalysis.