Histone Modification Screening using Liquid Chromatography, Trapped Ion Mobility Spectrometry, and Time-Of-Flight Mass Spectrometry.

Histone proteins are highly abundant and conserved among eukaryotes and play a large role in gene regulation as a result of structures known as posttranslational modifications (PTMs). Identifying the position and nature of each PTM or pattern of PTMs in reference to external or genetic factors allows this information to be statistically correlated with biological responses such as DNA transcription, replication, or repair. In the present work, a high-throughput analytical protocol for the detection of histone PTMs from biological samples is described. The use of complementary liquid chromatography, trapped ion mobility spectrometry, and time-of-flight mass spectrometry (LC-TIMS-ToF MS/MS) enables the separation and PTM assignment of the most biologically relevant modifications in a single analysis. The described approach takes advantage of recent developments in dependent data acquisition (DDA) using parallel accumulation in the mobility trap, followed by sequential fragmentation and collision-induced dissociation. Histone PTMs are confidently assigned based on their retention time, mobility, and fragmentation pattern.

Comparative site-specific N-glycoproteome analysis reveals aberrant N-glycosylation and gives insights into mannose-6-phosphate pathway in cancer.

N-glycosylation is implicated in cancers and aberrant N-glycosylation is recognized as a hallmark of cancer. Here, we mapped and compared the site-specific N-glycoproteomes of colon cancer HCT116 cells and isogenic non-tumorigenic DNMT1/3b double knockout (DKO1) cells using Fbs1-GYR N-glycopeptide enrichment technology and trapped ion mobility spectrometry. Many significant changes in site-specific N-glycosylation were revealed, providing a molecular basis for further elucidation of the role of N-glycosylation in protein function. HCT116 cells display hypersialylation especially in cell surface membrane proteins. Both HCT116 and DKO1 show an abundance of paucimannose and 80% of paucimannose-rich proteins are annotated to reside in exosomes. The most striking N-glycosylation alteration was the degree of mannose-6-phosphate (M6P) modification. N-glycoproteomic analyses revealed that HCT116 displays hyper-M6P modification, which was orthogonally validated by M6P immunodetection. Significant observed differences in N-glycosylation patterns of the major M6P receptor, CI-MPR in HCT116 and DKO1 may contribute to the hyper-M6P phenotype of HCT116 cells. This comparative site-specific N-glycoproteome analysis provides a pool of potential N-glycosylation-related cancer biomarkers, but also gives insights into the M6P pathway in cancer.

Increasing the throughput of sensitive proteomics by plexDIA.

Current mass spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. To increase the throughput of sensitive proteomics, we developed an experimental and computational framework, called plexDIA, for simultaneously multiplexing the analysis of peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using three-plex non-isobaric mass tags, plexDIA enables quantification of threefold more protein ratios among nanogram-level samples. Using 1-hour active gradients, plexDIA quantified ~8,000 proteins in each sample of labeled three-plex sets and increased data completeness, reducing missing data more than twofold across samples. Applied to single human cells, plexDIA quantified ~1,000 proteins per cell and achieved 98% data completeness within a plexDIA set while using ~5 minutes of active chromatography per cell. These results establish a general framework for increasing the throughput of sensitive and quantitative protein analysis.

HLAII peptide presentation of infliximab increases when complexed with TNF.

CD4+ T-cell activation through recognition of Human Leukocyte Antigen II (HLAII)-presented peptides is a key step in the development of unwanted immune response against biotherapeutics, such as the generation of anti-drug antibodies (ADA). Therefore, the identification of HLAII-presented peptides derived from biotherapeutics is a crucial part of immunogenicity risk assessment and mitigation strategies during drug development. To date, numerous CD4+ T-cell epitopes have been identified by HLAII immunopeptidomics in antibody-based biotherapeutics using either their native or aggregated form. Antibody-target immune complexes have been detected in patients with ADA and are thought to play a role in ADA development by enhancing the presentation of CD4+ T-cell epitopes at the surface of antigen presenting cells (APCs). The aim of this study was to investigate the effect of biotherapeutic antibody-target immune complexes on the HLAII peptide presentation of biotherapeutics in human primary monocyte-derived dendritic cells (DCs). The trimeric tumor necrosis factor (TNF) and its biotherapeutic antagonists infliximab (INFL), adalimumab (ADAL), and a single armed Fab' were used as a model system. The HLAII immunopeptidome of DCs loaded with antagonists or their immune complexes with TNF was analyzed by trapped ion mobility time-of-flight mass spectrometry (timsTOF MS) leading to the identification of ~ 12,000 unique HLAII-associated peptides per preparation. Anti-TNF sequences were detected at a median of 0.3% of the total immunopeptidome, against a majority background of peptides from endogenous and media-derived proteins. TNF antagonist presentation spanned the variable and constant regions in a widespread manner in both light and heavy chains, consistent with previously discovered HLAII peptides. This investigation extends the collection of observed HLAII peptides from anti-TNF biotherapeutics to include sequences that at least partially span the complementary determining regions (CDRs), such as the LCDR1 for both INFL and ADAL. Although antagonist presentation varied significantly across donors, peptides from both bivalent antagonists INFL and ADAL were more highly presented relative to the Fab'. While TNF immune complexes did not alter overall HLAII presentation, a moderate increase in presentation of a subset of peptide clusters was observed in the case of INFL-TNF, which included HCDR2, HCDR3 and LCDR2 sequences.

PRM-LIVE with Trapped Ion Mobility Spectrometry and Its Application in Selectivity Profiling of Kinase Inhibitors.

Parallel reaction monitoring (PRM) has emerged as a popular approach for targeted protein quantification. With high ion utilization efficiency and first-in-class acquisition speed, the timsTOF Pro provides a powerful platform for PRM analysis. However, sporadic chromatographic drift in peptide retention time represents a fundamental limitation for the reproducible multiplexing of targets across PRM acquisitions. Here, we present PRM-LIVE, an extensible, Python-based acquisition engine for the timsTOF Pro, which dynamically adjusts detection windows for reproducible target scheduling. In this initial implementation, we used iRT peptides as retention time standards and demonstrated reproducible detection and quantification of 1857 tryptic peptides from the cell lysate in a 60 min PRM-LIVE acquisition. As an application in functional proteomics, we use PRM-LIVE in an activity-based protein profiling platform to assess binding selectivity of small-molecule inhibitors against 220 endogenous human kinases.

Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants.

Current public health guidelines on physical activity and sleep duration are limited by a reliance on subjective self-reported evidence. Using data from simple wrist-worn activity monitors, we developed a tailored machine learning model, using balanced random forests with Hidden Markov Models, to reliably detect a number of activity modes. We show that physical activity and sleep behaviours can be classified with 87% accuracy in 159,504 minutes of recorded free-living behaviours from 132 adults. These trained models can be used to infer fine resolution activity patterns at the population scale in 96,220 participants. For example, we find that men spend more time in both low- and high- intensity behaviours, while women spend more time in mixed behaviours. Walking time is highest in spring and sleep time lowest during the summer. This work opens the possibility of future public health guidelines informed by the health consequences associated with specific, objectively measured, physical activity and sleep behaviours.

Automatic assignment of metal-containing peptides in proteomic LC-MS and MS/MS data sets.

Transition metal-containing proteins and enzymes are critical for the maintenance of cellular function and metal-based (metallo)drugs are commonly used for the treatment of many diseases, such as cancer. Detection and characterisation of metallodrug targets is crucial for improving drug-design and therapeutic efficacy. Due to the unique isotopic ratios of many metal species, and the complexity of proteomic samples, standard MS data analysis of these species is unsuitable for accurate assignment. Herein a new method for differentiating metal-containing species within complex LCMS data is presented based upon the Smart Numerical Annotation Procedure (SNAP). SNAP-LC accounts for the change in isotopic envelopes for analytes containing non-standard species, such as metals, and will accurately identify, record, and display the particular spectra within extended LCMS runs that contain target species, and produce accurate lists of matched peaks, greatly assisting the identification and assignment of modified species and tailored to the metals of interest. Analysis of metallated species obtained from tryptic digests of common blood proteins after reactions with three candidate metallodrugs is presented as proof-of-concept examples and demonstrates the effectiveness of SNAP-LC for the fast and accurate elucidation of metallodrug targets.

Association of bovine dentine phosphophoryn with collagen fragments.

Bovine dentine phosphophoryn (BDP), a protein rich in aspartyl (Asp) and O-phosphoseryl (Ser(P)) residues, is synthesized by odontoblasts and believed to be involved in matrix-mediated biomineralization of dentine. Phosphophoryn was purified from bovine dentine using EDTA extraction, Ca(2+) precipitation, anion exchange and size exclusion chromatography. The purified protein migrated on SDS-PAGGE as a single band. The protein was dephosphorylated using a chelex alkaline dialysis procedure, repurified using anion exchange and size exclusion chromatography and then subjected to cleavage with trypsin. The digest was subjected to reversed-phase HPLC and analysed by Q-TOF mass spectrometry. The only non-trypsin peptides that could be identified were two collagen Type I alpha2 peptides whose sequence was determined by fragmentation analysis. The association of collagen fragments with highly purified phosphophoryn suggests that the EDTA extraction method yields BDP that is strongly bound to collagen fragments. This association now helps explain discrepancies in molecular weight and amino acid composition data for various phosphophoryn preparations compared with the same data calculated from the C-terminal extension of mouse, rat and human dentine sialophosphoprotein (DSPP) gene products. Analysis of the mutation pattern of the clinical disorder Osteogenesis Imperfecta within the region enclosed by the identified collagen fragments reveals that phosphophoryn associates with a segment of collagen that is crucial for structure and/or function.