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auf dem Keller, U.

Publications and source records attributed to auf dem Keller, U..

15 recordsLinked to original sources
bioRxiv2026

Rhomboid protease RHBDL2 is a calcium-activated suppressor of EGFR signalling in keratinocytes.

Signalling via the epidermal growth factor receptor (EGFR) is indispensable for morphogenesis and tissue homeostasis. It is activated by extracellular ligands, typically released from transmembrane precursors by proteolysis. Ligand shedding activity is provided by the conserved rhomboid intramembrane serine proteases in Drosophila, but by the unrelated ADAM family metalloproteases in mammals, leaving the functions of mammalian non-mitochondrial rhomboids underexplored. Using quantitative proteomics, we show that EGFR is the main endogenous substrate of the human rhomboid protease RHBDL2 in keratinocytes. By shedding the EGFR ectodomain, thus producing a decoy receptor, RHBDL2 suppresses EGFR signalling, limiting cell migration and invasion. Conspicuously, RHBDL2 activity is upregulated by elevated intracellular calcium concentration, a condition typical for keratinocyte differentiation. These effects are recapitulated in primary human keratinocytes, and human skin equivalents deficient in RHBDL2 display incomplete differentiation and are morphologically disordered compared to wild type cells. We propose that context-specific fine-tuning of EGFR signalling and sensitivity to cross-talk from other signalling pathways could be important and hitherto overlooked roles of rhomboid proteases in mammals.

cell biology↗
bioRxiv2026

Mapping Tissue-Specific Protease Dynamics in the Pig Respiratory Tract During Influenza A Virus Infection

Influenza A virus (IAV) is a zoonotic pathogen capable of infecting diverse avian and mammalian hosts, causing seasonal epidemics and occasional global pandemics in humans. Viral entry requires proteolytic activation of hemagglutinin (HA). While serine proteases such as TMPRSS2, and HAT are known HA activators, the respiratory tract harbours additional proteases whose contributions to infection remain unclear. Dysregulation of these proteases can enhance viral replication, tissue damage, and inflammation, highlighting the need for a systems-level view of the proteolytic landscape. Here, we use high-throughput, proteome-wide proteomics and N-terminomics to identify 112 host proteases across the nasal mucosa, trachea, and lung. We monitor and validate 28 proteases with targeted proteomics and microfluidic qPCR, representing a comprehensive degradome analysis in the respiratory tract of the highly translational pig model of influenza infection. We show that protease abundance and activity were highly tissue-specific: while the nasal mucosa showed selective activation of broad- and narrow-specificity proteases alongside robust antiviral responses, the trachea exhibited modest modulation with subtle shifts in protease-inhibitor balance, and the lung maintained predominantly active proteases despite lower viral loads but severe tissue damage, indicative of immune-mediated pathology. Sequence motif analysis revealed distinct cleavage preferences across tissues, indicating differential protease processing across the studied respiratory tissues in antiviral pathways, antigen processing, and tissue remodelling. Several identified proteases, including ST14, KLKB1, PRSS8, and LGMN, were increased and functionally active upon infection, suggesting roles in viral processing and host immune regulation. Collectively, our results define a spatially organised proteolytic network that shapes tissue-specific antiviral host responses and contributes to H1N1 influenza pathogenesis.

immunology↗
bioRxiv2025

Persistent ECM Scarring Reprograms Intestinal Stem Cells to Drive Chronic Inflammation

Tissue regeneration is conventionally viewed as a return to homeostasis. Here, we uncover that the extracellular matrix (ECM) in the colon undergoes a lasting pathological reprogramming following inflammation, forming a remodeled niche--modECM--that persistently disrupts intestinal stem cell (ISC) identity. Using temporal multi-omics, biomechanical profiling, and spatial fate mapping in murine colitis models, we show that modECM, characterized by Collagen XVIII accumulation and immune-driven proteolysis, redirects ISCs toward a wound-associated, squamous-like epithelial state with pro-inflammatory transcriptional signatures. Ex vivo, modECM alone reprograms ISC fate by suppressing Wnt signaling and activating immune recruitment pathways. In vivo, modECM-rich zones sustain T cell infiltration and KRT14 epithelial cell emergence from Lgr5 progenitors. This aberrant epithelial program is mirrored in inflamed rectal biopsies from ulcerative colitis patients. Our findings redefine the ECM as a long-lived instructive compartment that encodes injury memory and promotes maladaptive regeneration, positioning it as a potential therapeutic target in chronic inflammatory disease.

immunology↗
bioRxiv2025

Pathological meprin α expression associated with degradation of dermokine drives a psoriasis-like skin phenotype in a genetic mouse model

Keratinocyte proliferation and differentiation is regulated via proteolytic networks. Dysregulation of proteases within these networks can cause hyperproliferative and inflammatory skin disorders. In healthy skin the metalloprotease meprin is localized in the stratum basale. In contrast, in wound healing tissue and psoriatic lesions increased meprin levels are found in the upper epidermal layers. We developed a transgenic mouse model for inducible expression of pathological meprin levels (K5M) to investigate its epidermal degradome and identify molecular links to keratinocyte proliferation and skin inflammation. K5M mice developed a severe skin phenotype characterized by hyperkeratosis, acanthosis and parakeratosis accompanied by increased transepidermal water loss and a strong inflammatory response within six days after induction of meprin overexpression. Histological and molecular analyses showed that increasing meprin expression correlates with meprin activity and keratinocyte hyperproliferation. Proteomics analyses revealed massive changes in proteins associated with keratinocyte differentiation and epidermal barrier integrity already three days after induction. N-terminomics data indicated a dominant chymotryptic activity with elevated proteolytic turnover of proteins associated with the cytoskeleton, cellular stress responses and cell adhesion. By filtering for cleavage sites that match with the specificity of meprin , we identified highly elevated dermokine-derived peptides. Subsequent mass spectrometric analyses validated dermokine as a novel substrate of meprin and identified the cleavage site, which is highly conserved in mammals. Based on the striking similarities with the phenotype reported for dermokine {beta}{gamma}-/- mice, we propose meprin as a central regulator of keratinocyte proliferation and leukocyte recruitment by proteolytic inactivation of dermokine. Hence, pathological meprin activity could be a driver of hyperproliferative, inflammatory skin disorders like psoriasis vulgaris.

molecular biology↗
bioRxiv2025

Multi-omics analysis of keratinocytes reveals dermokine-dependent regulation of cell-cell adhesion via p120

Loss of keratinocyte differentiation is a leading cause in several skin diseases and needs to be controlled in adult homeostasis by for instance growth factors and proteases. Among them, we studied the role of isoform-rich dermokine - a wound- and tumour-related matrix metalloproteinase 10 substrate - via functional multi-omics. We generated dermokine isoform-dependent keratinocyte knockouts and three dimensional (3D) organotypic skin cultures and analyzed changes in their proteome and phosphoproteome by quantitative mass spectrometry. Through functional in vitro assays, we demonstrate that in the absence of dermokine-isoforms, p120 phosphorylation increases while cell-cell adhesion decreases in keratinocytes. Furthermore, we validate the link between decreased dermokine expression and phosphorylated p120-mediated adhesion in non-healing wounds samples derived from patients. Our data reveal a novel dermokine-p120-dependent cell-cell adhesion phenotype in keratinocytes and improve our understanding of wound-edge keratinocytes, expanding the hypothesis that dysregulated wounds resemble cancer.

cell biology↗
bioRxiv2023

CLIPPER 2.0: Peptide level annotation and data analysis for positional proteomics

Positional proteomics methodologies have transformed protease research, and have brought mass spectrometry (MS)-based degradomics studies to the forefront of protease characterization and system-wide interrogation of protease signaling. Considerable advancements in sensitivity and throughput of liquid chromatography (LC)-MS/MS instrumentation enable generation of enormous positional proteomics datasets of natural and protein termini and neo-termini of cleaved protease substrates. However, such progress has not been observed to the same extent in data analysis and post-processing steps, which arguably constitute the largest bottleneck in positional proteomics workflows. Here, we present a computational tool, CLIPPER 2.0, that builds on prior algorithms developed for MS-based protein termini analysis, facilitating peptide level annotation and data analysis. CLIPPER 2.0 can be used with several sample preparation workflows and proteomics search algorithms, and enables fast and automated database information retrieval, statistical and network analysis, as well as visualization of terminomic datasets. We demonstrate our tool by analyzing GluC and MMP9 cleavages in HeLa lysates. CLIPPER 2.0 is available at https://github.com/UadKLab/CLIPPER-2.0. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=192 SRC="FIGDIR/small/569335v1_ufig1.gif" ALT="Figure 1"> View larger version (83K): org.highwire.dtl.DTLVardef@16d603borg.highwire.dtl.DTLVardef@1c8d9d0org.highwire.dtl.DTLVardef@1540246org.highwire.dtl.DTLVardef@614c9a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗
bioRxiv2023

De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments

Bottom-up mass spectrometry-based proteomics is challenged by the task of identifying the peptide that generates a tandem mass spectrum. Traditional methods that rely on known peptide sequence databases are limited and may not be applicable in certain contexts. De novo peptide sequencing, which assigns peptide sequences to the spectra without prior information, is valuable for various biological applications; yet, due to a lack of accuracy, it remains challenging to apply this approach in many situations. Here, we introduce InstaNovo, a transformer neural network with the ability to translate fragment ion peaks into the sequence of amino acids that make up the studied peptide(s). The model was trained on 28 million labelled spectra matched to 742k human peptides from the ProteomeTools project. We demonstrate that InstaNovo outperforms current state-of-the-art methods on benchmark datasets and showcase its utility in several applications. Building upon human intuition, we also introduce InstaNovo+, a multinomial diffusion model that further improves performance by iterative refinement of predicted sequences. Using these models, we could de novo sequence antibody-based therapeutics with unprecedented coverage, discover novel peptides, and detect unreported organisms in different datasets, thereby expanding the scope and detection rate of proteomics searches. Finally, we could experimentally validate tryptic and non-tryptic peptides with targeted proteomics, demonstrating the fidelity of our predictions. Our models unlock a plethora of opportunities across different scientific domains, such as direct protein sequencing, immunopeptidomics, and exploration of the dark proteome. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/555055v3_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@20063org.highwire.dtl.DTLVardef@1679c01org.highwire.dtl.DTLVardef@1332940org.highwire.dtl.DTLVardef@1d9339f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗
bioRxiv2023

Salmonella-driven Intestinal Edema in Mice is Characterized by Tensed Fibronectin Fibers

Intestinal edema is a common manifestation of numerous gastrointestinal diseases and is characterized by the accumulation of fluid in the interstitial space of the intestinal wall. Technical advances in laser capture microdissection and low-biomass proteomics now allow us to specifically characterize the intestinal edema proteome. Our data identifies a high abundance of antimicrobial factors, but also highlights major contributions from the blood clotting system, extracellular matrix (ECM) and protease - protease inhibitor networks. The ECM is a complex fibrillar network of macromolecules that provides structural and mechanical support to the intestinal tissue. One abundant component of the ECM observed in the Salmonella-driven intestinal edema is the mechanosensitive glycoprotein fibronectin. Using mechanosensitive staining of fibronectin reveals a solely tensed molecular conformation of fibronectin fibers present in the edema, in contrast to other cecal tissue areas, despite the high abundance of proteases able to cleave fibronectin. Co-staining for fibrin(ogen) indicates the formation of a provisional matrix, similar to what is observed in response to skin injury. The absence of low tensional fibronectin fibers and the additional finding of a high number of protease inhibitors in the edema proteome could indicate a critical role of stretched fibronectin fibers in maintaining tissue integrity in the severely inflamed cecum. Understanding these processes may provide valuable functional diagnostic markers of intestinal disease progression. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/550515v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1318c52org.highwire.dtl.DTLVardef@1f35d21org.highwire.dtl.DTLVardef@67370borg.highwire.dtl.DTLVardef@4ec7e6_HPS_FORMAT_FIGEXP M_FIG C_FIG

pathology↗
bioRxiv2023

Assessing the role of trypsin in quantitative plasma- and single-cell proteomics towards clinical application

Mass spectrometry-based bottom-up proteomics is rapidly evolving and routinely applied in large biomedical studies. Proteases are a central component of every bottom-up proteomics experiment, digesting proteins into peptides. Trypsin has been the most widely applied protease in proteomics, due to its characteristics. With ever-larger cohort sizes and possible future clinical application of mass spectrometry-based proteomics, the technical impact of trypsin becomes increasingly relevant. To assess possible biases introduced by trypsin digestion, we evaluated the impact of eight commercially available trypsins in a variety of bottom-up proteomics experiments, and across a range of protease concentrations and storage times. To investigate the universal impact of these technical attributes, we included bulk HeLa-cell lysate, human plasma and single HEK293 cells, which were analyzed over a range of Selected Reaction Monitoring (SRM), Data-Independent Acquisition (DIA), and Data-Dependent Acquisition (DDA) instrument methods on three LC-MS instruments. Quantification methods employed encompassed both label-free approaches and absolute quantification utilizing spike-in heavy-labeled recombinant protein fragment standards. Based on this extensive dataset, we report variations between commercial trypsins, their source, as well as their concentration. Furthermore, we provide suggestions on the handling of trypsin in large scale studies.

molecular biology↗
bioRxiv2023

A Comparative Study of Protein Structure Prediction Tools for Challenging Targets: Snake Venom Toxins

Protein structure determination is a critical aspect of biological research, enabling us to understand protein function and potential applications. Recent advances in deep learning and artificial intelligence have led to the development of several protein structure prediction tools, such as AlphaFold2 and ColabFold. However, their performance has primarily been evaluated on well-characterised proteins, and comparisons using proteins with poor reference templates are lacking. In this study, we evaluated three modelling tools on their prediction of over 1000 snake venom toxin structures with no reference templates. Our findings show that AlphaFold2 (AF2) performed the best across all assessed parameters. We also observed that ColabFold (CF) only scored slightly worse than AF2, while being computationally less intensive. All tools struggled with regions of intrinsic disorder, such as loops and propeptide regions, and performed well in predicting the structure of functional domains. Overall, our study highlights the importance of exercising caution when working with proteins that have poor reference templates, are large, and contain flexible regions. Nonetheless, leveraging computational structure prediction tools can provide valuable insights into the modelling of protein interactions with different targets and reveal potential binding sites, active sites, and conformational changes, as well as into the design of potential molecular binders for reagent, diagnostic, or therapeutic purposes. StatementRecent advances in machine learning have led to the development of new protein structure prediction tools. However, these tools have mainly been tested on well-known proteins and their performance on proteins without known templates is unclear. This study evaluated the performance of three tools on over 1000 snake venom toxins. We found that while caution is required when studying poorly characterised proteins, these tools offer valuable opportunities to understand protein function and applications.

bioinformatics↗
bioRxiv2022

Enhancing single-cell proteomics through tailored Data-Independent Acquisition and micropillar array-based chromatography

Single-cell resolution analysis of complex biological tissues is fundamental to capture cell-state heterogeneity and distinct cellular signaling patterns that remain obscured with population-based techniques. The limited amount of material encapsulated in a single cell however, raises significant technical challenges to molecular profiling. Due to extensive optimization efforts, mass spectrometry-based single-cell proteomics (scp-MS) has emerged as a powerful tool to facilitate proteome profiling from ultra-low amounts of input, although further development is needed to realize its full potential. To this end, we carried out comprehensive analysis of orbitrap-based data independent acquisition (DIA) for limited material proteomics. Notably, we found a fundamental difference between optimal DIA methods for high- and low-load samples. We further improved our low-input DIA method by relying on high-resolution MS1 quantification, thus more efficiently utilizing available mass analyzer time. With our ultra-low input tailored DIA method, we were able to accommodate long injection times and high resolution, while keeping the scan cycle time low enough to ensure robust quantification. Finally, we establish a complete experimental scp-MS workflow, combining DIA with accessible single-cell sample preparation and the latest chromatographic and computational advances and showcase our developments by profiling real single cells.

systems biology↗
bioRxiv2020

Mapping specificity, entropy, allosteric changes and substrates in blood proteases by a high-throughput protease screen

Proteases are among the largest protein families in eukaryotic phylae with more than 500 genetically encoded proteases in humans. By cleaving a wide range of target proteins, proteases are critical regulators of a vast number of biochemical processes including apoptosis and blood coagulation. Over the last 20 years, knowledge of proteases has been drastically expanded by the development of proteomic approaches to identify and quantify proteases and their substrates. In spite of their merits, some of these methods are laborious, not scalable or incompatible with native environments. Consequentially, a large number of proteases remain poorly characterized. Here, we introduce a simple proteomic method to profile protease activity based on isolation of protease products from native lysates using a 96FASP filter and their analysis in a mass spectrometer. The method is significantly faster, cheaper, technically less demanding, easily multiplexed and produces accurate protease fingerprints in near-native conditions. By using the blood cascade proteases as a case study we obtained protease substrate profiles of unprecedented depth that can be reliably used to map specificity, entropy and allosteric changes of the protease and to design fluorescent probes and predict physiological substrates. The native protease characterization method is comparable in performance, but largely exceeds the throughput of current alternatives.

biochemistry↗
bioRxiv2019

A Quantitative Single-Cell Proteomics Approach to Characterize an Acute Myeloid Leukemia Hierarchy

Large-scale single-cell analyses are of fundamental importance in order to capture biological heterogeneity within complex cell systems, but have largely been limited to RNA-based technologies. Here we present a comprehensive benchmarked experimental and computational workflow, which establishes global single-cell mass spectrometry-based proteomics as a tool for large-scale single-cell analyses. By exploiting a primary leukemia model system, we demonstrate both through pre-enrichment of cell populations and through a non-enriched unbiased approach that our workflow enables the exploration of cellular heterogeneity within this aberrant developmental hierarchy. Our approach is capable of consistently quantifying approximately 1000 proteins per cell across thousands of individual cells using limited instrument time. Furthermore, we developed a computational workflow (SCeptre) that effectively normalizes the data, integrates available FACS data and facilitates downstream analysis. The approach presented here lays a solid foundation for implementing global single-cell proteomics studies across the world.

systems biology↗
bioRxiv2019

Tendon response to matrix unloading is determined by the patho-physiological niche

Aberrant matrix turnover with elevated matrix proteolysis is a hallmark of tendon pathology. While tendon disease mechanisms remain obscure, mechanical cues are central regulators. Unloading of tendon explants in standard culture conditions provokes rapid cell-mediated tissue breakdown. Here we show that biological response to tissue unloading depends on the mimicked physiological context. Our experiments reveal that explanted tendon tissues remain functionally stable in a simulated avascular niche of low temperature and oxygen, regardless of the presence of serum. This hyperthermic and hyperoxic niche-dependent catabolic switch was shown by whole transcriptome analysis (RNA-seq) to be a strong pathological driver of an immune-modulatory phenotype, with a stress response to reactive oxygen species (ROS) and associated activation of catabolic extracellular matrix proteolysis that involved lysosomal activation and transcription of a range of proteolytic enzymes. Secretomic and degradomic analysis through terminal amine isotopic labeling of substrates (TAILS) confirmed that proteolytic activity in unloaded tissues was strongly niche dependent. Through targeted pharmacological inhibition we isolated ROS mediated oxidative stress as a major checkpoint for matrix proteolysis. We conclude from these data that the tendon stromal compartment responds to traumatic mechanical unloading in a manner that is highly dependent on the extrinsic niche, with oxidative stress response gating the proteolytic breakdown of the functional collagen backbone.

molecular biology↗