Biology subjects

Impens, F.

Publications and source records attributed to Impens, F..

At least 19 recordsLinked to original sources

bioRxiv2026

Organ identity shapes autophagy dynamics and selectivity in plants

Plants, with their unique evolutionary trajectory and complex physiological adaptations, have developed organ-specific mechanisms to cope with various environmental stresses. Autophagy, an essential catabolic process, plays an important role in maintaining plant growth, immunity, and overall fitness. In this study, we reveal the spatio-temporal dynamics of autophagic responses in Arabidopsis thaliana roots and shoots under different stress conditions, including AZD8055 treatment and carbon and nitrogen depletion. Our findings demonstrate that roots exhibit stronger autophagic activity than shoots under all three conditions, highlighting the unique adaptations of these two organs. Furthermore, we dissect the selectivity of autophagy in targeting organelles, revealing immediate, delayed, and no uptake categories. Additionally, we observe organelles coexisting within autophagic bodies, shedding light on the complexity of cargo selection. This study enhances our understanding of plant specific autophagy dynamics, emphasizing its role in sustaining the source-sink functions and offering insights into plant adaptation to diverse stressors.

plant biology↗

bioRxiv2026

A novel nanobody-based approach for targeting heterogeneous Acinetobacter baumannii isolates and closely related pathogenic Acinetobacter spp.

Acinetobacter baumannii is a top-priority, ESKAPE pathogen that poses a major challenge to human health. The pathogen is difficult to combat due to its extensive arsenal of antibiotic resistance and its protective polysaccharide capsule. In addition, A. baumannii isolates are highly heterogeneous, which complicates the development of rapid detection methods or novel targeted therapeutic approaches. Here, we discovered and characterized a new biotechnological tool, the nanobody H7 (NbH7), along with its conserved target, the surface-exposed Omp25 protein of A. baumannii, and elucidated their interaction at the molecular level. Moreover, we demonstrate that NbH7-functionalized magnetic beads enable selective and efficient capture of A. baumannii from bacterial mixtures, including non-pathogenic intestinal bacteria. This provides proof of concept for a new targeting system that remains effective across diverse A. baumannii clinical isolates and capsule types and holds potential for use in diagnostic cell enrichment and targeted therapies.

molecular biology↗

bioRxiv2026

Proteomic profiling of whole tissue sections in cardiac ATTR amyloidosis reveals increased extracellular matrix remodeling

Cardiac transthyretin amyloidosis (ATTR-CA) is caused by myocardial deposition of misfolded transthyretin, leading to progressive heart failure. Disease pathology, however, extends beyond passive amyloid deposition and also involves active processes such as extracellular matrix (ECM) remodeling and immune activation. Mass spectrometry (MS) is the gold standard for amyloid typing in diagnostics. Here, we applied quantitative MS-driven proteomics on formalin-fixed paraffin-embedded whole cardiac tissue sections from six ATTR-CA cases, ten unaffected controls and four AL-CA controls to investigate protein expression changes. In addition to transthyretin, over 500 proteins were upregulated in ATTR-CA biopsies, including complement and coagulation factors as well as extracellular matrix (ECM) remodeling proteins. Among these, members of the A Disintegrin and Metalloproteinase with Thrombospondin Motifs (ADAMTS) family, metalloproteinases (MMPs), and Tissue Inhibitor of Metalloproteinases (TIMP3) showed significant upregulation. These proteins are key regulators of ECM turnover and structural integrity. Immunohistochemistry confirmed ADAMTS4 enrichment in amyloid deposits, while TIMP3 showed strong expression in cardiomyocytes and weaker staining within amyloid deposits. Together, these findings indicate that ECM remodeling, alongside complement and coagulation activation, represents a reproducible feature of cardiac ATTR amyloidosis. Whole-tissue proteomics provides biological insights that extend beyond amyloid typing, with potential implications for biomarker discovery and therapeutic targeting in ATTR-CA.

pathology↗

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↗

bioRxiv2025

Identification of DksA as a novel pro-inflammatory mediator of Pseudomonas aeruginosa under conditions mimicking chronic cystic fibrosis lung infection

Chronic infection with Pseudomonas aeruginosa is a major driver of airway inflammation, which plays a central role in the progression of cystic fibrosis (CF) lung disease. During long-term colonization, P. aeruginosa adapts to the CF lung by downregulating virulence factors and adopting a biofilm-associated, mucoid lifestyle. Despite the expected reduction in immune activation due to these adaptations, excessive inflammation persists, a paradox that remains poorly understood. Our objective was to identify novel bacterial mediators sustaining persistent inflammation by P. aeruginosa in the CF lung. To this end, we analyzed clinical P. aeruginosa CF isolates, cultured them in synthetic CF sputum medium, and exposed 3D lung epithelial cell cultures to the resulting cell-free supernatants. There was considerable variability in pro-inflammatory activity among the isolates, with a subset of the isolates inducing strong IL-8 secretion by the 3D cells despite low production of known virulence factors. Comparative proteomics analysis of the cell-free supernatants of pro- and anti-inflammatory isolates revealed several mediators not previously linked to inflammation. Thirteen of these candidate pro-inflammatory mediators were selected for further analysis. Using P. aeruginosa transposon mutants lacking the respective mediators, DksA (a transcription factor) was confirmed as an immunomodulatory mediator in the 3D lung model. Finally, analysis of existing transcriptomes of P. aeruginosa in CF sputum, revealed that dksA was found to be one of the most strongly expressed genes in this patient population, highlighting the relevance of our findings. In conclusion, we identified a novel P. aeruginosa mediator that may contribute to CF airway inflammation.

microbiology↗

bioRxiv2025

Proximity biotinylation at the host-Shigella interface reveals UFMylation as an antibacterial pathway.

Host cells contest invasion by intracellular bacterial pathogens with multiple strategies that recognise and / or damage the bacterial surface. To identify novel host defence factors targeted to intracellular bacteria, we developed a versatile proximity biotinylation approach coupled to quantitative mass spectrometry that maps the host-bacterial interface during infection. Using this method, we discovered that intracellular Shigella and Salmonella become targeted by UFM1-protein ligase 1 (UFL1), an E3 ligase that catalyses the covalent attachment of Ubiquitin-fold modifier 1 (UFM1) to target substrates in a process called UFMylation. We show that Shigella antagonises UFMylation in a dual manner: first, using its lipopolysaccharide (LPS) to shield from UFL1 recruitment; second, preventing UFM1 decoration by the bacterial effector IpaH9.8. Absence of UFMylation leads to an increase of bacterial burden in both human cells and zebrafish larvae, suggesting that UFMylation is a highly conserved antibacterial pathway. Contrary to canonical ubiquitylation, the protective role of UFMylation is independent of autophagy. Altogether, our proximity mapping of the host-bacterial interface identifies UFMylation as an ancient antibacterial pathway and holds great promise to reveal other cell-autonomous immunity mechanisms.

microbiology↗

bioRxiv2025

Data-independent immunopeptidomics discovery of low-abundant bacterial epitopes

Mass spectrometry-based immunopeptidomics is a powerful approach to uncover peptides presented by human leukocyte antigen (HLA) molecules that can guide vaccine design and immunotherapies. While data-dependent acquisition (DDA) has been the standard for navigating through the complexity associated with non-enzymatic immunopeptide database searches, data-independent acquisition (DIA) is increasingly adopted in immunopeptidomics research. In this work, we compare diaPASEF to conventional ddaPASEF in terms of global immunopeptidome profiling and bacterial epitope discovery of the model intracellular pathogen Listeria monocytogenes. We show that DIA spectrum-centric workflows that search pseudo-MS/MS spectra complement DDA analysis by uncovering additional human and bacterial immunopeptides. Furthermore, we leveraged DIA-NN for generating and searching proteome-wide predicted HLA class I peptide spectral libraries, scoring approximately 150 million immunopeptide peptide precursors. This approach outperformed other spectrum-based methods in identification of MHC class I peptides and recovered low-abundant peptide precursors missed by other methods. Taken together, our results demonstrate how both DIA spectrum- and peptide-centric immunopeptidomics analysis are promising strategies to identify low-abundant immunopeptides.

molecular biology↗

bioRxiv2025

Collisional cross-section prediction for multiconformational peptide ions with IM2Deep

Peptide collisional cross-section (CCS) prediction is complicated by the tendency of peptide ions to exhibit multiple conformations in the gas phase. This adds further complexity to downstream analysis of proteomics data, for example for identification or quantification through feature finding. Here, we present an improved version of IM2Deep that is trained on a carefully curated dataset to predict CCS values of multiconformational peptides. The training data is derived from a large and comprehensive set of publicly available datasets. This comprehensive training dataset together with a tailored architecture allows for the accurate CCS prediction of multiple peptide conformational states. Furthermore, the enhanced IM2Deep model also retains high precision for peptides with a single observed conformation. IM2Deep is publicly available under a permissive open source license at https://github.com/compomics/IM2Deep.

bioinformatics↗

bioRxiv2025

Decoding Phenotypic Variability in Osteogenesis Imperfecta: Zebrafish as a Model for Molecular and Ultrastructural Insights.

Phenotypic variability is common in human diseases, even when the same genes are affected. In this study, three zebrafish models of Osteogenesis Imperfecta (OI) with dominant glycine substitutions in type I collagen genes (col1a1amh13/+, col1a1adc124/+, and col1a2mh15/+) were characterized for phenotypic severity and variability, using a newly developed standardized scoring system. Comprehensive analyses of the vertebral columns in these models revealed histological and ultrastructural differences that corresponded with phenotypic severity. Increasing skeletal severity correlated with a higher incidence of skeletal deformities and abnormalities. This, in turn, was associated with thinner bones and increased disorganization of collagen fibrils, fiber accumulation and mineralization, elastin deposits, and increased cell proliferation in the notochord and intervertebral ligament (IVL). Additionally, osteoblast function and bone regenerative capacity were increasingly compromised. These characteristics, combined with genetic information, have the potential to predict the severity of phenotypic outcomes in dominant forms of OI, caused by mutations in type I collagen. A remarkable intra-familial phenotypic variability in the col1a2mh15/+mutant holds potential for future approaches that could help in understanding the underlying mechanisms of this variability and the identification of modifier genes. Finally, through proteomics analysis three potential protein biomarkers (HSP47, Col8a1, and Bcan) were identified, that could serve as indicators of disease severity. These biomarkers not only have diagnostic value, but will allow stratification by OI type, have predictive value towards progression of the clinical presentation and will play a role in treatment guidance. Validation in human tissue samples will further reveal their clinical relevance. Significance StatementPhenotypic variability in human diseases, such as Osteogenesis Imperfecta (OI), remains poorly understood. Using zebrafish models with dominant glycine substitutions in type I collagen, this study links genetic mutations to phenotypic severity through standardized scoring and detailed ultrastructural and molecular analyses. Key findings include skeletal abnormalities, compromised osteoblast function, and intra-familial phenotypic variability, suggesting the role of modifier genes. Proteomics identified three potential biomarkers (HSP47, Col8a1, and Bcan) with diagnostic and prognostic value. These results provide critical insights into genotype-phenotype correlations, offering a foundation for personalized approaches to diagnosis, stratification, and treatment of OI and related disorders.

molecular biology↗

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↗

bioRxiv2024

A multi-year longitudinal harmonization of quality controls in mass spectrometry proteomics core facilities

Quality control procedures play a pivotal role in ensuring the reliability and consistency of data generated in mass spectrometry-based proteomics laboratories. However, the lack of standardized quality control practices across laboratories poses challenges for data comparability and reproducibility. In response, we conducted a harmonization study within proteomics laboratories of the Core for Life alliance with the aim to establish a common quality control framework, which facilitates comprehensive quality assessment and identification of potential sources of performance drift. Through collaborative efforts, we developed a consensus quality control standard for longitudinal assessment and we adopted a common processing software. We generated a 4-year longitudinal dataset from multiple instruments and laboratories, which enabled us to assess intra- and inter-laboratory variability, to identify causes of performance drift, and to establish community reference values for several quality control parameters. Our study enhances data comparability and reliability and fosters a culture of collaboration and continuous improvement within the proteomics community to ensure the integrity of proteomics data.

biochemistry↗

bioRxiv2024

Ubiquitin-specific protease 8 controls B cell proteostasis and cell survival in multiple myeloma

Ubiquitin-specific protease 8 (USP8) is a multifunctional deubiquitinating enzyme that plays a pivotal role in the regulation of endosomal and lysosomal trafficking. Several studies showed that USP8 is critically involved in the pathogenesis of various tumor entities. Recently, USP8 emerged as a vulnerability gene in multiple myeloma (MM), suggesting a functional role in the B- and plasma cell compartment. Here we have comprehensively analyzed mice with deletion of Usp8 at different stages of B-cell development. Furthermore, we evaluated the function of USP8 in proteasome inhibition susceptible and Bortezomib (BTZ) resistant patient derived MM cells using depletion of USP8 and treatment with DUB-IN-2, a widely used inhibitor published to target USP8. Usp8 depletion in Usp8f/fCd19-Cre mice affected B-cell survival and development favoring immature, innate-like B cells, and germinal center and plasma cells, while also elevating immune-responses and causing Roquin depletion. Cells expressing catalytically inactive USP8 accumulated proteins modified with mixed ubiquitin/NEDD8 chains, indicating proteotoxic stress. Moreover, we identified these modifications as preferred substrates of USP8. In MM cells, efficient USP8 knockdown reduced survival by inducing lysosomal dysfunction. In contrast, DUB-IN-2 induced an enhanced ER stress response to treatment with BTZ. Our results highlight the potential of targeting USP8 and the combination of DUB-IN-2 and BTZ in treating BTZ-resistant MM. However, our biochemical and cellular analyses raise fundamental concerns about the function of DUB-IN-2 as a USP8 inhibitor.

molecular biology↗

bioRxiv2024

High-throughput nano-flow proteomics using a dual-column electrospray source

With current trends in proteomics, especially regarding clinical and low input (to single cell) samples, it is increasingly important to both maximize the throughput of the analysis and maintain as much sensitivity as possible. The new generation of mass spectrometers (MS) are taking a huge leap in sensitivity, allowing to analyze samples with shorter liquid chromatography (LC) methods while digging as deep in the proteome. However, the throughput can be doubled by implementing a dual column nano-LC-MS configuration. For this purpose, we used a dual-column setup with a two-outlet electrospray source, and compared it to a classic dual-column setup with a single-outlet source.

biochemistry↗

bioRxiv2023

Integrating multiple omics levels using the human protein complexome as a framework, a multi-omics study of inborn errors of metabolism

Proteins organize into functional assemblies that drive diverse biological activities. Leveraging a comprehensive dataset of manually curated annotations for the human protein complexome, we investigated biological perturbations at the protein complex level. Using proteomics and transcriptomics data from fibroblasts of patients with inborn errors of metabolism (IEM) and control samples, we globally mapped information onto complex subunits to discern affected processes. Across the patient cohort (consisting of organic acidaemias, fatty acid oxidation defects and mitochondrial respiratory chain defect IEMs), mitochondrial oxidative phosphorylation emerged as the most perturbed pathway, identified through proteomics datasets. Simultaneously, metabolomics highlighted significant regulation of phospholipids in patients with Fatty Acid and Mitochondrial IEM. Moreover, proteomics analysis also revealed the dysregulation of protein complexes involved in histone (de)acetylation, a finding validated through Western Blot analysis measuring histone acetylation levels. This introduces a novel epigenetic dimension to IEM and metabolic research, suggesting avenues for further exploration. Our study demonstrates a multiomics data integration concept that maps proteomics and transcriptomics data onto model organism complexomes. This integrative approach can be extended to metabolomics and lipidomics, associating information with complexes having metabolic functions, such as enzymatic complexes. This global strategy for identifying disease-relevant perturbations offers a systems-wide perspective on molecular-level physiological and pathological changes. Such insights are crucial for devising clinical intervention strategies and prioritizing druggable pathways and complexes. The presented methodology provides a foundation for future investigations, emphasizing the importance of integrating multiomics data to comprehensively understand cellular machinery alterations and facilitate targeted therapeutic approaches.

biochemistry↗

bioRxiv2023

CysQuant: simultaneous quantification of cysteine oxidation and protein abundance using data dependent or independent acquisition mass spectrometry

Protein cysteinyl thiols are susceptible to reduction-oxidation reactions that can influence protein function. Accurate quantification of cysteine oxidation is therefore crucial for decoding protein redox regulation. Here, we present CysQuant, a novel approach for simultaneous quantification of cysteine oxidation degrees and protein abundancies. CysQuant involves light/heavy iodoacetamide isotopologues for differential labeling of reduced and reversibly oxidized cysteines analyzed by data-dependent acquisition (DDA) or data-independent acquisition mass spectrometry (DIA-MS). Using plexDIA with in silico predicted spectral libraries, we quantified an average of 18% cysteine oxidation in Arabidopsis thaliana by DIA-MS, including a subset of highly oxidized cysteines forming disulfide bridges in AlphaFold2 predicted structures. Applying CysQuant to Arabidopsis seedlings exposed to excessive light, we successfully quantified the well-established increased reduction of Calvin-Benson cycle enzymes and discovered yet uncharacterized redox-sensitive disulfides in chloroplastic enzymes. Overall, CysQuant is a highly versatile tool for assessing the cysteine modification status that can be widely applied across various mass spectrometry platforms and organisms.

molecular biology↗

bioRxiv2023

Benchmarking DIA data analysis workflows

Data independent acquisition (DIA) has become a well-established method in LC-MS driven proteomics. Nonetheless, there are still a lot of possibilities at the data analysis level. By benchmarking different DIA analysis workflows using a ground-truth sample, consisting of a differential spike-in of UPS2 in a constant yeast background, we provide a roadmap for DIA data analysis of shotgun samples based on whether sensitivity, precision or accuracy is of the essence. Three different commonly used DIA software tools (DIA-NN, EncyclopeDIA and SpectronautTM) were tested in both spectral library mode and spectral library-free mode. In spectral library mode we used the independent spectral library prediction tools PROSIT and MS2PIP together with DeepLC, next to the classical DDA-based spectral libraries. In total we benchmarked 12 DIA workflows. DIA-NN in library-free mode or using in silico predicted libraries, together with Spectronaut in library-free mode, shows the highest sensitivity maintaining a high reproducibility and accuracy. In general, DIA-NN shows the best reproducibility, while the accuracy is comparable for all DIA workflows.

bioinformatics↗

bioRxiv2023

Aberrant phase separation is a common killing strategy of positively charged peptides in biology and human disease

Positively charged repeat peptides are emerging as key players in neurodegenerative diseases. These peptides can perturb diverse cellular pathways but a unifying framework for how such promiscuous toxicity arises has remained elusive. We used mass-spectrometry-based proteomics to define the protein targets of these neurotoxic peptides and found that they all share similar sequence features that drive their aberrant condensation with these positively charged peptides. We trained a machine learning algorithm to detect such sequence features and unexpectedly discovered that this mode of toxicity is not limited to human repeat expansion disorders but has evolved countless times across the tree of life in the form of cationic antimicrobial and venom peptides. We demonstrate that an excess in positive charge is necessary and sufficient for this killer activity, which we name polycation poisoning. These findings reveal an ancient and conserved mechanism and inform ways to leverage its design rules for new generations of bioactive peptides.

cell biology↗

bioRxiv2022

Immunopeptidomics-based design of highly effective mRNA vaccine formulations against Listeria monocytogenes

Listeria monocytogenes is a foodborne intracellular bacterial pathogen leading to human listeriosis. Despite a high mortality rate and increasing antibiotic resistance no clinically approved vaccine against Listeria is available. Attenuated Listeria strains offer protection and are tested as antitumor vaccine vectors, but would benefit from a better knowledge on immunodominant vector antigens. To identify novel antigens, we screened for Listeria epitopes presented on the surface of infected human cell lines by mass spectrometry-based immunopeptidomics. In between more than 15,000 human self-peptides, we detected 68 Listeria epitopes from 42 different bacterial proteins, including several known antigens. Peptide epitopes presented on different cell lines were often derived from the same bacterial surface proteins, classifying these antigens as potential vaccine candidates. Encoding these highly presented antigens in lipid nanoparticle mRNA vaccine formulations resulted in specific CD8+ T-cell responses and high levels of protection in vaccination challenge experiments in mice. Our results pave the way for the development of a clinical mRNA vaccine against Listeria and aid to improve attenuated Listeria vaccines and vectors, demonstrating the power of immunopeptidomics for next-generation bacterial vaccine development.

immunology↗