Biology subjects

Bonn, S.

Publications and source records attributed to Bonn, S..

At least 19 recordsLinked to original sources

bioRxiv2025

BioChemAIgent: An AI-driven Protein Modeling and Docking Framework for Structure-Based Drug Discovery

Recent advances in AI have substantially accelerated in silico drug discovery, yet most computational approaches remain task-specific and require expert knowledge. We present BioChemAIgent, an agentic framework that orchestrates state-of-the-art AI models and established computational chemistry tools to support end-to-end small-molecule analysis, protein modeling, molecular docking, and interaction analysis through a unified interface. BioChemAIgent emphasizes transparent reasoning, reproducible workflows, and community-oriented extensibility for structural biology and drug discovery applications.

bioinformatics↗

bioRxiv2025

Spatial Analysis of T Cell Clonality in Autoimmune Kidney Disease Using TRV Probes

Hypothesizing that the localization of T cell clones correlates with immune function, our goal was to develop an unbiased method to study the spatial distribution of T cells in tissues. We created an in situ hybridization panel with 248 probes that identify immune and tissue cell types, and 132 probes for all variable TRAV, TRBV, TRGV, and TRDV gene segments. Applying this approach to analyze renal biopsies from patients with autoimmune kidney disease, combinations of TRV segments provided spatial information about T cell clonality. Confined clusters of clonally related {beta} T cells were found in proximity to increased numbers of antigen-presenting cells, B cells, and other T cells, reflecting local immune cell interactions. {gamma}{delta} T cells were more frequently located outside or at their periphery of T cell infiltration areas. In conclusion, integrating spatial information with TCR clonotype analysis provided new insights into the organization of immune responses at the tissue level.

immunology↗

bioRxiv2025

Community-based biomedical context to unlock agentic systems

Large language models (LLMs) face reliability challenges stemming from hallucinations and insufficient access to validated scientific resources. Existing solutions are often fragmented and limited to specific applications, hindering broader adoption and interoperability. Here, we present Biomedical Context for Artificial Intelligence (BioContextAI), an open-source initiative centered on Model Context Protocol (MCP) servers to address these limitations. BioContextAI provides a community-oriented registry for discovering domain-specific MCP servers and a proof-of-concept server implementation that integrates widely-used biomedical knowledgebases. By enabling standardized access to validated scientific knowledge, BioContextAI aims to facilitate the development of composable agentic systems for biomedical research. Together, this work contributes to an emerging ecosystem of community-driven approaches for expanding the capabilities and reliability of biomedical AI systems.

systems biology↗

bioRxiv2025

SCALE: Unsupervised Multi-Scale Domain Identification in Spatial Omics Data

Single-cell spatial transcriptomics enables precise mapping of cellular states and functional domains within their native tissue environment. These functional domains often exist at multiple spatial scales, with larger domains encompassing smaller ones, reflecting the hierarchical organization of biological systems. However, the identification of these functional domain hierarchies has been hardly explored due to the lack of appropriate computational methods. In this work, we present SCALE, an unsupervised algorithm for multi-scale domain identification in spatial transcriptomics data. SCALE combines neural graph representation learning with an entropy-based search algorithm to detect functional domains at different scales. It reaches state-of-the-art performance in single- and multi-scale domain detection on simulated and murine brain Xenium and MERFISH data, as well as patient-derived kidney tissue, highlighting its robustness and scalability across diverse tissue types and platforms. SCALEs ease of use makes it a powerful aid for advancing our understanding of tissue organization and function in health and disease.

bioinformatics↗

bioRxiv2025

The diversity of cellular systems involved in carbonate precipitation by Escherichia coli

Climate change is increasing the need to limit levels of anthropogenic CO2 released into the atmosphere. One approach being investigated is to generate products based on microbially induced carbonate precipitation (MICP), which can trap CO2 as CaCO3. We recently identified a novel MICP pathway in bacteria that is initiated by Ca2+ toxicity in cells, causing extracellular CO2 to be trapped as CO 2- by Escherichia coli, although the yield of precipitated CaCO remained low (in the milligram range). In this work, we used the E. coli Keio gene knock-out library to identify 54 genes involved in MICP in E. coli, which could be broadly characterized into four groups: central metabolism, iron metabolism, cell architecture, and transport. The role of central metabolism appears to be crucial in maintaining alkaline conditions surrounding the cell that promote CaCO3 precipitation. The role of iron metabolism was less clear, although the results suggest that growth rate influences the initiation of MICP. While the impact of repeating polymeric structures on cell surfaces promoting MICP is well established, our results suggest that other structural features may play a role, including fimbriae and flagella. Finally, the results confirmed that Ca2+ transport is central to MICP under calcium stress. The results further suggest that the ZntB efflux pump may play a previously unidentified role in Ca2+ transport in E. coli. By overexpressing some of these genes, our work suggests that there are several previously unidentified cellular mechanisms that could serve as a target for enhanced MICP in E. coli. By incorporating these processes into MICP pathways in E. coli, it may be possible to increase the volume of CO2 fixed using this pathway and yield potentially new products that can replace CO2 intensive products, such as precipitated calcium carbonates (PCCs) for industry.

microbiology↗

bioRxiv2024

Spatio-temporal interaction of immune and renal cells determines glomerular crescent formation in autoimmune kidney disease

Rapidly progressive glomerulonephritis (RPGN) is the most aggressive group of autoimmune kidney disease with the worst prognosis. Anti-neutrophil cytoplasmic antibody (ANCA) associated vasculitis, anti-glomerular basement membrane (anti-GBM) and lupus nephritis are the most common causes of RPGN and are characterized by the formation of glomerular crescents and infiltration of leukocytes that eventually lead to glomerulosclerosis and kidney failure. In this work, we used high-resolution spatial transcriptomics of 32 ANCA, 19 lupus nephritis, 6 anti-GBM, and 6 control patients to understand how intercellular signaling between immune and renal tissue cells leads to renal inflammation and glomerular injury. Using 3,218,210 immune and kidney cells, we observed that the biological pathways involved in the sequence of glomerular crescent formation are similar across the diseases. While innate immune cells infiltrated the glomerular compartment relatively early, later increases in adaptive immune cells were largely restricted to the periglomerular regions. These changes in immune cells temporally correlated with increases in glomerular parietal epithelial (PEC) and fibrotic mesangial cells, suggesting disease-relevant functional signaling between these immune and renal cells. Cell communication analysis revealed early disease PDGF signaling from epithelial and mesangial cells to PECs, causing their activation and proliferation. At later stages, TGF-{beta} signaling from macrophages, T cells, epithelial cells, and mesangial cells to PECs triggered the expression of extracellular matrix components resulting in glomerulosclerosis. Our results highlight a spatio-temporally conserved progression into glomerular crescents and sclerosis for ANCA, lupus nephritis, and anti-GBM disease, which is driven by consecutive PDGF and TGF-{beta} signaling to PECs.

immunology↗

bioRxiv2024

Neddylation regulates the development and function of excitatory neurons

The development and function of neurons is orchestrated by a plethora of regulatory mechanisms that control the abundance, localization, interactions, and function of proteins. A key role in this regard is assumed by post-translational protein modifications (PTMs). While some PTM types, such as phosphorylation or ubiquitination, have been explored comprehensively, PTMs involving ubiquitin-like modifiers (Ubls) have remained comparably enigmatic (Ubls). This is particularly true for the Ubl Nedd8 and its conjugation to proteins, i.e. neddylation, in nerve cells. In the present study, we generated a conditional Nedd8 knock-out mouse line and examined the consequences of Nedd8-deletion in cultured postmitotic glutamatergic neurons. Our findings reveal that Nedd8-ablation in young glutamatergic neurons causes alterations in the expression of developmental transcription factors that control neuronal differentiation, ultimately leading to defects in the development of a mature glutamatergic neuronal phenotype. Apparent manifestations of these defects include increased vGlut2 expression levels, reduced vGlut1 and endophilin 1 expression levels, reduced dendrite complexity, and increased transmitter release probability. Collectively, our results highlight a pivotal role for neddylation in controlling the fate of glutamatergic neurons and excitatory synaptic transmission. HighlightsO_LIReduced dendrite complexity in Nedd8-deficient neurons C_LIO_LIIncreased synaptic vesicle fusion probability in Nedd8-deficient neurons C_LIO_LIAltered synaptic vesicle cycling in Nedd8-deficient neurons C_LIO_LINeddylation controls the maturation of glutamatergic neurons C_LI Graphical AbstractResearch Topic

neuroscience↗

bioRxiv2024

PCA-based spatial domain identification with state-of-the-art performance

The identification of biologically meaningful domains is a central step in the analysis of spatial transcriptomic data. Following Occams razor, we show that a simple PCA-based algorithm for spatial domain identification rivals the performance of ten competing state-of-the-art methods across six single-cell spatial transcriptomic datasets. Our reductionist approach, NichePCA, provides researchers with intuitive domain interpretation and excels in execution speed, robustness, and scalability.

bioinformatics↗

bioRxiv2024

Seumetry: a versatile and comprehensive R toolkit to accelerate high-dimensional flow and mass cytometry data analysis

Recent progress in flow and mass cytometry technologies enables the simultaneous measurement of over 50 parameters for an individual cell. The resulting increase in data volume and complexity present challenges, as conventional analysis methods based on manual gating are time-consuming and fail to capture unknown or minor cell populations. Advances in single-cell RNA sequencing (scRNAseq) technologies have prompted the development of sophisticated computational analysis tools specifically designed to process and analyze high-dimensional biological data, some of which could significantly improve certain aspects of cytometry data analysis. Building on these advances, we here present Seumetry, a framework that combines flow and mass cytometry data-specific analysis methods with the capabilities of Seurat, a powerful tool for the analysis of scRNAseq data. Seumetry offers advanced quality control, data visualizations, and differential population abundance and protein expression analysis. We tested Seumetry on an in-house generated complex dataset of immune cells from different layers of human intestines, demonstrating that Seumetry accurately identifies distinct immune cell populations. Furthermore, using a publicly available mass cytometry dataset, Seumetry recapitulates previously published results, further validating its use for high-dimensional flow and mass cytometry data. In summary, Seumetry provides a new scalable framework for the comprehensive analysis of high-dimensional cytometry data with seamless integration into commonly used scRNAseq analysis tools, enabling in-depth analysis methods to facilitate biological interpretations.

bioinformatics↗

bioRxiv2024

Podocyte exopher-formation as a novel pathomechanism in membranous nephropathy

BackgroundMembranous nephropathy (MN) is caused by autoantibody binding to podocyte foot process antigens such as THSD7A and PLA2R1. The mechanisms of the glomerular antigen/autoantibody deposition and clearance are unknown. MethodsWe explore the origin and significance of glomerular accumulations in (1) diagnostic and follow-up biospecimens from THSD7A+ and PLA2R1+-MN patients compared to nephrotic non-MN patients, and (2) in experimental models of THSD7A+-MN. ResultsWe discovered podocyte exophers as correlates of histological antigen/autoantibody aggregates found in the glomerular urinary space of MN patients. Exopher vesicle formation represents a novel form of toxic protein aggregate removal in Caenorhabditis elegans neurons. In MN patients, podocytes released exophers to the urine. Enrichment of exophers from MN patient urines established them as a glomerular exit route for antigens and bound autoantibody. Exophers also carried disease-associated proteins such as complement and provided a molecular imprint of podocyte injury pathways. In experimental THSD7A+-MN, exophers were formed from podocyte processes and cell body. Their formation involved the translocation of antigen/autoantibody from the subepithelial to the urinary side of podocyte plasma membranes. Urinary exopher-release correlated with lower albuminuria and lower glomerular antigen/autoantibody burden. In MN patients the prospective monitoring of urinary exopher abundance and of exopher-bound autoantibodies was additive in the assessment of immunologic MN activity. ConclusionsExopher-formation and release is a novel pathomechanism in MN to remove antigen/autoantibody aggregates from the podocyte. Tracking exopher-release will add a non-invasive diagnostic tool with prognostic potential to clinical diagnostics and follow-up of MN patients.

cell biology↗

bioRxiv2023

A universal preservation protocol for multi-omic and histological analysis of kidney tissue

Biobanking of tissue from clinically obtained kidney biopsies for later use with multi-omic and imaging techniques is an inevitable step to overcome the need of disease model systems and towards translational medicine. Hence, collection protocols ensuring integration into daily clinical routines using preservation media not requiring liquid nitrogen but instantly preserving kidney tissue for clinical and scientific analyses are of paramount importance. Thus, we modified a robust single nucleus dissociation protocol for kidney tissue stored snap frozen or in the preservation media RNAlater and CellCover. Using porcine kidney tissue as surrogate for human kidney tissue, we conducted single nucleus RNA sequencing with the Chromium 10X Genomics platform. The resulting data sets from each storage condition were analyzed to identify any potential variations in transcriptomic profiles. Furthermore, we assessed the suitability of the preservation media for additional analysis techniques (proteomics, metabolomics) and the preservation of tissue architecture for histopathological examination including immunofluorescence staining. In this study, we show that in daily clinical routines the RNAlater facilitates the collection of highly preserved kidney biopsies and enables further analysis with cutting-edge techniques like single nucleus RNA sequencing, proteomics, and histopathological evaluation. Only metabolome analysis is currently restricted to snap frozen tissue. This work will contribute to build tissue biobanks with well-defined cohorts of the respective kidney disease that can be deeply molecularly characterized, opening new horizons for the identification of unique cells, pathways and biomarkers for the prevention, early identification, and targeted therapy of kidney diseases.

genomics↗

bioRxiv2023

Multiomic ALS signatures highlight sex differences and molecular subclusters and identify the MAPK pathway as therapeutic target

Amyotrophic lateral sclerosis (ALS) is the most common motor neuron disease and lacks effective disease-modifying treatments. Here, we performed a multiomic analysis of the prefrontal cortex of 51 patients with sporadic ALS and 50 control subjects, as well as four transgenic mouse models of C9orf72-, SOD1-, TDP-43-, and FUS-ALS to characterize early and sex-specific disease mechanisms in ALS. Integrated analyses of transcriptomes, (phospho)proteomes, and miRNAomes revealed more pronounced changes in males. We identified transcriptome-based human ALS subclusters driven by the immune response, ECM, mitochondrial respiration, and RNA metabolism. The molecular signatures of human subclusters were reflected in specific mouse models. Individual and integrative multiomics analysis highlighted the mitogen-activated protein kinase pathway as an early disease-relevant mechanism. Its modulation by trametinib in vitro and in vivo validated that mitogen-activated protein kinase kinase 2 is a promising therapeutic target with beneficial effects in females.

neuroscience↗

bioRxiv2023

Epigenetic neural glioblastoma enhances synaptic integration and predicts therapeutic vulnerability

Neural-tumor interactions drive glioma growth as evidenced in preclinical models, but clinical validation is nascent. We present an epigenetically defined neural signature of glioblastoma that independently affects patients survival. We use reference signatures of neural cells to deconvolve tumor DNA and classify samples into low- or high-neural tumors. High-neural glioblastomas exhibit hypomethylated CpG sites and upregulation of genes associated with synaptic integration. Single-cell transcriptomic analysis reveals high abundance of stem cell-like malignant cells classified as oligodendrocyte precursor and neural precursor cell-like in high-neural glioblastoma. High-neural glioblastoma cells engender neuron-to-glioma synapse formation in vitro and in vivo and show an unfavorable survival after xenografting. In patients, a high-neural signature associates with decreased survival as well as increased functional connectivity and can be detected via DNA analytes and brain-derived neurotrophic factor in plasma. Our study presents an epigenetically defined malignant neural signature in high-grade gliomas that is prognostically relevant.

neuroscience↗

bioRxiv2022

Performance comparison of TCR-pMHC prediction tools reveals a strong data dependency

The interaction of T-cell receptors with peptide-major histocompatibility complex molecules plays a crucial role in adaptive immune responses. Currently there are various models aiming at predicting TCR-pMHC binding, while a standard dataset and procedure to compare the performance of these approaches is still missing. In this work we provide a general method for data collection, preprocessing, splitting and generation of negative examples, as well as comprehensive datasets to compare TCR-pMHC prediction models. We collected, harmonized, and merged all the major publicly available TCR-pMHC binding data and compared the performance of five state-of-the-art deep learning models (TITAN, NetTCR, ERGO, DLpTCR and ImRex) using this data. Our performance evaluation focuses on two scenarios: 1) different splitting methods for generating training and testing data to assess model generalization and 2) different data versions that vary in size and peptide imbalance to assess model robustness. Our results indicate that the five contemporary models do not generalize to peptides that have not been in the training set. We can also show that model performance is strongly dependent on the data balance and size, which indicates a relatively low model robustness. These results suggest that TCR-pMHC binding prediction remains highly challenging and requires further high quality data and novel algorithmic approaches.

bioinformatics↗

bioRxiv2022

Single-cell transcriptomics reveals a mechanosensitive injury signaling pathway in early diabetic nephropathy

Diabetic nephropathy (DN) is the leading cause of end-stage renal disease and histopathologic glomerular lesions are among the earliest structural alterations of DN. However, the signaling pathways that initiate these glomerular alterations are incompletely understood. To delineate the cellular and molecular basis for DN initiation, we performed single-cell and bulk RNA sequencing of renal cells from type 2 diabetes mice (BTBR ob/ob) at the early stage of DN. Analysis of differentially expressed genes revealed glucose-independent responses in glomerular cell types. The gene regulatory network upstream of glomerular cell programs suggested the activation of mechanosensitive transcriptional pathway MRTF-SRF predominantly taking place in mesangial cells. Importantly, activation of MRTF-SRF transcriptional pathway was also identified in DN glomeruli in independent patient cohort datasets. Furthermore, ex vivo kidney perfusion suggested that the regulation of MRTF-SRF is a common mechanism in response to glomerular hyperfiltration. Overall, our study presents a comprehensive single-cell transcriptomic landscape of early DN, highlighting mechanosensitive signaling pathways as novel targets of diabetic glomerulopathy.

molecular biology↗

bioRxiv2022

Transcriptional and clonal characterization of cytotoxic CD8+ T cells in crescentic glomerulonephritis

Crescentic glomerulonephritis (cGN), most often caused by anti-neutrophil cytoplasmic autoantibody (ANCA)-associated vasculitis, is an aggressive form of immune-mediated kidney disease and represents an important cause of end-stage renal failure. Although it is known that T cells infiltrate the kidney in cGN, their precise role in autoimmune kidney disease remains to be fully elucidated. By performing single-cell analysis, we identified activated, clonally expanded CD8+ T cells with a cytotoxic gene expression profile in the kidneys of patients with ANCA-associated cGN. Using an experimental model of cGN, we demonstrated that clonally expanded murine CD8+ T cells highly expressed the cytotoxic molecule granzyme B. Moreover, lack of CD8+ T cells or granzyme B resulted in an ameliorated course of cGN. This was associated with reduced cleaved caspase-3 induction in renal tissue cells. Our data indicate that clonally expanded cytotoxic CD8+ T cells have a previously unrecognized pathogenic function in aggravating immune-mediated kidney disease.

immunology↗

bioRxiv2022

GM-CSF drives immune-mediated glomerular disease by licensing monocyte-derived cells to produce MMP12

Glomerulonephritis is a group of immune-mediated diseases that cause inflammation within the glomerulus and adjacent compartments of the kidney and is a major cause of end-stage renal disease. T cells are among the main drivers of glomerulonephritis. However, the T cell subsets, cytokine networks, and downstream effector mechanisms that lead to renal tissue injury are largely unknown, which has hindered the development of targeted therapies. Here we identify a population of GM-CSF-producing T cells that accumulates in the kidneys of patients with ANCA-associated glomerulonephritis, infiltrates the renal tissue in a mouse model of glomerulonephritis, and promotes tissue destruction and loss of renal function. Mechanistically, we show that GM-CSF producing T cells licence monocyte-derived cells to produce matrix metalloproteinase 12 (MMP12), which cleaves components of the glomerular basement membrane and exacerbates renal pathology. These findings provide a mechanistic rationale for the immunopathology of T cell-mediated diseases and identify the "GM-CSF - monocyte-derived cells - MMP12" pathway as a promising therapeutic target in treatment of glomerulonephritis.

immunology↗

bioRxiv2022

DiSCERN - Deep Single Cell Expression ReconstructioN for improved cell clustering and cell subtype and state detection

Single cell sequencing provides detailed insights into biological processes including cell differentiation and identity. While providing deep cell-specific information, the method suffers from technical constraints, most notably a limited number of expressed genes per cell, which leads to suboptimal clustering and cell type identification. Here we present DISCERN, a novel deep generative network that reconstructs missing single cell gene expression using a reference dataset. DISCERN outperforms competing algorithms in expression inference resulting in greatly improved cell clustering, cell type and activity detection, and insights into the cellular regulation of disease. We used DISCERN to detect two unseen COVID-19-associated T cell types, cytotoxic CD4+ and CD8+ Tc2 T helper cells, with a potential role in adverse disease outcome. We utilized T cell fraction information of patient blood to classify mild or severe COVID-19 with an AUROC of 81% that can serve as a biomarker of disease stage. DISCERN can be easily integrated into existing single cell sequencing workflows and readily adapted to enhance various other biomedical data types.

bioinformatics↗