Biology subjects

Van Moortel, L.

Publications and source records attributed to Van Moortel, L..

5 recordsLinked to original sources

bioRxiv2026

A platform for high-throughput and ultrasensitive immunopeptidomics

Mass spectrometry (MS)-based immunopeptidomics is a powerful approach for untargeted discovery of peptides presented on major histocompatibility complex (MHC) molecules, which can guide the selection of vaccine antigens and immunotherapy targets. First-generation immunopeptidomics workflows require processing of hundreds of millions of cells using lengthy, manual procedures. More recent approaches focus on increasing either sensitivity or throughput, but rarely combine both aspects. Here, we describe a semi-automated immunopeptidomics platform that combines high sensitivity with high throughput by implementing highly optimized conditions for immunoprecipitation, elution and purification of MHC class I and II peptides on a 96-well positive-pressure device. Upon analysis of 25% of the eluate from 16 million cells, our workflow identified over 13,500 MHC I and 6,000 MHC II peptides on a timsTOF SCP mass spectrometer, operating in DDA-PASEF mode. Exploring the sensitivity limits of our platform, we identified over 1,000 MHC I peptides from as few as 20,000 JY cells. Validating the platforms performance for quantitative biological discovery, we report the identification of known and novel bacterial immunopeptides from U937 macrophages infected with Listeria monocytogenes or Bacillus Calmette-Guerin (BCG). Together, our optimized immunopeptidomics platform enables robust immunopeptide detection from lower-input samples in a high-throughput fashion, enabling its use for biological applications where sample amounts are limiting.

immunology↗

bioRxiv2024

Maximizing immunopeptidomics-based bacterial epitope discovery by multiple search engines and rescoring

Mass spectrometry-based discovery of bacterial immunopeptides presented by infected cells allows untargeted discovery of bacterial antigens that can serve as vaccine candidates. However, reliable identification of bacterial epitopes is challenged by their extreme low abundance. Here, we describe an optimized bioinformatical framework to enhance the confident identification of bacterial immunopeptides. Immunopeptidomics data of cell cultures infected with Listeria monocytogenes were searched by four different search engines, PEAKS, Comet, Sage and MSFragger, followed by data-driven rescoring with MS2Rescore. Compared to individual search engine results, this integrated workflow boosted immunopeptide identification by an average of 27% and led to the high-confidence detection of 18 additional bacterial peptides (+27%) matching 15 different Listeria proteins (+36%). Despite the strong agreement between the search engines, a small number of spectra (< 1%) had ambiguous matches to multiple peptides and were excluded to ensure high-confident identifications. Finally, we demonstrate our workflow with sensitive timsTOF SCP data acquisition and find that rescoring, now with inclusion of ion mobility features, identifies 76% more peptides compared to Q Exactive HF acquisition. Together, our results demonstrate how integration of multiple search engine results along with data-driven rescoring maximizes immunopeptide identification, boosting the detection of high-confidence bacterial epitopes for vaccine development.

molecular biology↗

bioRxiv2023

Leveraging a self-cleaving peptide for tailored control in proximity labeling proteomics

Protein-protein interactions play an important biological role in every aspect of cellular homeostasis and functioning. Proximity labeling mass spectrometry-based proteomics overcomes challenges typically associated with other methods, and has quickly become the current state-of-the-art in the field. Nevertheless, tight control of proximity labeling enzymatic activity and expression levels is crucial to accurately identify protein interactors. Here, we leverage a T2A self-cleaving peptide and a non-cleaving mutant to accommodate the protein-of-interest in the experimental and control TurboID setup. To allow easy and streamlined plasmid assembly, we built a Golden Gate modular cloning system to generate plasmids for transient expression and stable integration. To highlight our T2A Split-link design, we applied it to identify protein interactions of the glucocorticoid receptor and SARS-CoV-2 nucleocapsid and NSP7 proteins by TurboID proximity labeling. Our results demonstrate that our T2A split-link provides an opportune control that builds upon previously established control requirements in the field. MotivationIn proximity labeling proteomics protein-protein interactions are identified by in vivo biotinylation. However, the current lack of a universally applicable negative control for differential analysis affects accurate mapping of the interactome. To bridge this gap, we conceptualized a system based on the T2A self-cleaving peptide to match expression levels between control and bait protein setups while using the same bait protein. In addition, we implemented a versatile modular cloning system to build mammalian expression vectors for, but not limited to, proximity labeling assays.

biochemistry↗

bioRxiv2023

Antagonism and selective modulation of the human glucocorticoid receptor both reduce recruitment of p300/CBP and the Mediator complex

Exogenous glucocorticoids are frequently used to treat inflammatory disorders and as adjuncts for treatment of solid cancers. However, their use is associated with severe side effects and therapy resistance. Novel glucocorticoid receptor (GR) ligands with a patient-validated reduced side effect profile have not yet reached the clinic. GR is a member of the nuclear receptor family of transcription factors and heavily relies on interactions with coregulator proteins for its transcriptional activity. To elucidate the role of the GR interactome in the differential transcriptional activity of GR following treatment with agonists, antagonists, or lead selective GR agonists and modulators (SEGRAMs), we generated comprehensive interactome maps by high-confidence proximity proteomics in lung epithelial carcinoma cells. We found that the GR antagonist RU486 and the SEGRAM Dagrocorat both reduced GR interaction with CREB-binding protein (CBP)/p300 and the Mediator complex when compared to the full GR agonist Dexamethasone. Our data offer new insights into the role of differential coregulator recruitment in shaping ligand-specific GR-mediated transcriptional responses. In BriefGlucocorticoids are commonly prescribed for the treatment of inflammatory disorders but are associated with severe side effects. Novel glucocorticoid receptor (GR) ligands with strong anti-inflammatory effects but reduced side effects are still sought after. Despite decades-long GR research, there is still an incomplete understanding of the molecular mechanisms driving context-specific GR activity. Using proximity labeling proteomics, we identified CREB-binding protein (CBP), p300 and the Mediator complex as potential crucial GR coregulators driving ligand-induced changes in GRs transcriptional activity. HighlightsO_LIGlucocorticoids (GCs), potent anti-inflammatory agents, can elicit side effects C_LIO_LIMore selective GCs, causing less side effects, are currently still unavailable C_LIO_LILack of fundamental insights on context-specific actions of the GC receptor (GR) C_LIO_LIWe mapped ligand-specific GR interactomes using proximity labeling proteomics C_LIO_LIp300/CBP and Mediator undergo ligand-dependent changes in interaction with GR C_LI

molecular biology↗

bioRxiv2022

Novel assays monitoring direct glucocorticoid receptor protein activity exhibit high predictive power for ligand activity on endogenous gene targets

Exogenous glucocorticoids are widely used in the clinic for the treatment of inflammatory disorders and auto-immune diseases. Unfortunately, their use is hampered by many side effects and therapy resistance. Efforts to find more selective glucocorticoid receptor (GR) agonists and modulators (called SEGRAMs) that are able to separate anti-inflammatory effects via gene repression from metabolic effects via gene activation, have been unsuccessful so far. In this study, we characterized a set of functionally diverse GR ligands in A549 cells, first using a panel of luciferase-based reporter gene assays evaluating GR-driven gene activation and gene repression. We expanded this minimal assay set with novel luciferase-based read-outs monitoring GR protein levels, GR dimerization and GR Serine 211 (Ser211) phosphorylation status and compared their outcomes with compound effects on the mRNA levels of known GR target genes in A549 cells and primary hepatocytes. We found that luciferase reporters evaluating GR-driven gene activation and gene repression were not always reliable predictors for effects on endogenous target genes. Remarkably, our novel assay monitoring GR Ser211 phosphorylation levels proved to be the most reliable predictor for compound effects on almost all tested endogenous GR targets, both driven by gene activation and repression. The integration of this novel assay in existing screening platforms running both in academia and industry may therefore boost chances to find novel GR ligands with an actual improved therapeutic benefit.

pharmacology and toxicology↗