Biology subjects

Kramann, R.

Publications and source records attributed to Kramann, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Cyclic uremia without kidney injury drives cardiovascular disease with rapid onset of immunosenescence and heart failure

End-stage kidney disease (ESKD) is a major driver of cardiovascular disease (CVD) and requires dialysis treatment leading to a unique metabolic phenotype with cyclic transitions between accumulation and clearance of uremic toxins. Although ESKD patients display the highest cardiovascular mortality among CKD patients, the effect of cyclic uremia on cardiovascular disease remains subject to discussion. Existing mouse models rely on induction of kidney injury and do not resolve the interconnected pathophysiological effects of metabolic, hypertensive and endocrine renal failure. They are therefore unable to distinguish uremia-driven effects from well-established drivers of cardiovascular disease such as hypertension. Here, we established a simple, reproducible, and sex-inclusive mouse model of uremia in absence of kidney injury or hypertension leveraging a bistable vesico-peritoneal shunt (VPS). Strikingly, we identify cyclic uremia as an independent driver of ESKD-induced CVD, that induces heart failure with preserved ejection fraction (HFpEF), vascular inflammation and immunosenescence. As such, the VPS represents the first preclinical model that enables investigation of cyclic uremia as a driver of cardiovascular disease.

systems biology↗

bioRxiv2026

Cyst-type epithelial heterogeneity shapes therapeutic responsiveness in ADPKD

Autosomal dominant polycystic kidney disease (ADPKD) exhibits substantial interpatient variability in disease course and therapeutic response, but the cellular basis for this variability remains poorly understood. Here, we combine single-nucleus RNA sequencing of human cyst epithelia with machine learning-based histological analysis of >1,800 cysts to resolve three epithelial cyst types--proximal tubule-like, collecting duct-like, and mixed. These cyst types display distinct injury states, metabolic programs, and stromal microenvironments, including a mixed-cyst niche enriched for CCL2-associated inflammatory signaling. Expression of key therapeutic targets was highly cell-type specific with CFTR enriched in proximal-like epithelia, whereas AVPR2 expression was confined to AQP2-positive collecting duct-like cells. Cyst-type composition varied widely across patients and in an orthologous mouse model (Pkd1RC/RC) in which the burden of AQP2-positive cysts correlated with responsiveness to tolvaptan. These findings identify cyst-type heterogeneity as a major determinant of molecular pathway activation and predictability of therapeutic response in ADPKD.

molecular biology↗

bioRxiv2026

Loss of nuclear-cytoplasmic compartmentalization is a hallmark of aging in podocytes

The nuclear envelope, with its nuclear pore complexes, establishes a selective barrier that maintains the spatial organization of the cellular proteome. Whether this barrier remains intact in long-lived postmitotic cells during aging is largely unknown. Here, we show that aging podocytes experience a progressive loss of nuclear-cytoplasmic compartmentalization. Using subcellular proteomics and quantitative imaging, we identify a global redistribution of proteins between the nucleus and cytoplasm, affecting key regulators of RNA processing, chromatin organization, and cellular metabolism. Loss of compartmentalization is accompanied by mitochondrial dysfunction, increased reactive oxygen species production, and aberrant nuclear accumulation of YAP1, linking nuclear barrier failure to metabolic and transcriptional dysregulation. These findings identify nuclear envelope dysfunction as a driver of proteome disorganization and cellular decline in aging podocytes and suggest that loss of nuclear-cytoplasmic compartmentalization represents a general mechanism contributing to dysfunction in long-lived cells.

cell biology↗

bioRxiv2026

Oncodevelopmental plasticity of the skeleton in myeloid neoplasms

Myelofibrosis in patients with myeloproliferative neoplasms (MPNs) is traditionally characterized by bone marrow fibrosis and osteosclerosis, with de novo bone formation commonly attributed to impaired osteoclast-mediated resorption. Here, we challenge this paradigm by demonstrating that a solitary clonal driver mutation simultaneously induces pathological bone formation and resorption, with osteosclerosis acting to conceal localized and active bone destruction rather than inhibiting it. Through population analysis; clinical imaging; patient-derived multi-tissue sequencing; murine models and organ-on-a-chip systems, we demonstrate that spatial and ontogeny-dependent remodeling in mesoderm- and neural crest-derived bones is mechanistically interconnected via a previously unidentified osteochondral stromal injury program. Neural crest-derived stromal cells suppress osteogenic programs and undergo injury-induced lineage plasticity with ectopic chondrogenesis, mirroring pathological remodeling in mesoderm-derived growth plate regions. This shared injury response promotes osteoclastogenesis and is mediated by a conserved Thrombospondin 1+ (THBS1+) stromal population that links fibrotic remodeling to bone loss. Combined pharmacological inhibition of THBS1 and JAK signaling reduces myeloproliferation, halts fibrosis progression, and restores two developmentally distinct bones, establishing THBS1 as a unifying therapeutic target in myelofibrosis.

cancer biology↗

bioRxiv2026

NicheSphere reveals Spp1⁺ macrophages as central hubs coordinating fibrotic remodeling in myeloproliferative neoplasms

Bone marrow fibrosis in myeloproliferative neoplasms arises from interactions between mutant hematopoietic clones and fibrosis-driving stromal cells. We identify Spp1 macrophages as central "communication hubs" integrating inflammatory and fibrotic programs via spatial proximity, ECM signaling, and cytokine activation. Using dual lineage-tracing, single-cell and multiplet RNA-sequencing, and a novel computational method for cell-colocalization and communication analysis named NicheSphere, we show that Spp1 macrophages form core communication hubs with osteoCAR cells, fibroblasts, and megakaryocytes. NicheSphere uncovered two distinct niches: macrophage-enriched compartments driving WNT, JAK-STAT, and TNF cytokine signaling, and a fibrosis-interacting core enriched in TGF-{beta} and ECM glycoproteins. Genetic ablation revealed cooperative roles of stromal and hematopoietic Spp1 in sustaining fibrosis and inflammation. Mechanistically, SPP1 promoted integrin-mediated adhesion, IL-1{beta} secretion, and stromal activation, while IL-1 cytokines induced Spp1 and collagen expression. Loss of Spp1 in hematopoietic progenitors reduced inflammation and restored macrophage function, establishing SPP1 macrophages as therapeutic targets in progressive bone marrow fibrosis.

cancer biology↗

bioRxiv2026

Kidney disease reprograms microbiome-host signaling to promote heart failure

BackgroundHeart failure is prevalent in chronic kidney disease (CKD) and linked to chronic inflammation. CKD-typical gut microbiome dysbiosis may stimulate inflammation, as bacterial aromatic metabolites are highly abundant and engage transcriptional programs through the aryl hydrocarbon receptor (AhR). Whether this axis drives cardiac remodeling and is therapeutically targetable remains unknown. MethodsWe used the subtotal nephrectomy model (STNx) and microbiome depletion by oral antibiotics. We investigated cardiac and renal function, AhR activity, metabolite profiles, and immunophenotypes by flow cytometry and transcriptomics. Candidate metabolite indoxyl sulfate (IxS) was tested in experimental HFpEF. In vivo and in vitro AhR inhibition (AhRi) was performed using a clinically tested compound. Mechanistic studies were performed in primary human and murine cardiac fibroblasts and T cells, as well as translational validation using UK Biobank data. ResultsMicrobiome depletion lowered bacterial metabolites and attenuated cardiac fibrosis and diastolic dysfunction in STNx, identifying AhR-driven expansion of interleukin-17A (IL-17A)-producing T helper cells (TH17) as key effector. Plasma IL-17A was stage-dependently elevated in CKD patients, particularly in HFpEF, and associated with all-cause mortality. Bacterial metabolite IxS promoted TH17 polarization and exacerbated cardiac dysfunction in HFpEF. AhRi using a small molecule inhibitor reduced TH17 abundance and attenuated cardiac fibrosis in STNx. Mechanistically, AhR and IL-17A signaling synergistically induced a conserved pro-fibrotic phenotype in human and murine cardiac fibroblasts, and AhR inhibition blocked ECM production in response to CKD patient serum. ConclusionA microbiome-AhR-IL-17A axis drives CKD-associated cardiac fibrosis. AhRi prevents remodeling, highlighting a potential therapeutic avenue to prevent cardiorenal multimorbidity.

immunology↗

bioRxiv2025

Pathway-Centric Visualization of Cell-Cell Communication in Single-Cell Transcriptomics Data

ObjectiveSingle-nuclei transcriptomics enables investigating ligand-receptor mediated cell-cell communication between different cell-types. However, current tools do not allow for non-programmatic means to access this analysis. Additionally, most methods can not account for molecular pathways involved in downstream receptor signaling in cell-cell communication. MethodsWe developed scVizComm, a ShinyApp based web-portal that can be used to interactively visualize ligand-receptor networks across cell-types and different conditions. ResultsscVizComm can be used to host pathway centric pre-calculated cell-cell communication results. Using human kidney and heart single-nuclei transcriptomics data, we showcase the utility of scVizComm in understanding ligand-receptor interactions involved in fibrosis related pathways. ConclusionscVizComm allows interactive visualisation and pathway centric prioritisation of cell-cell communication analyses.

bioinformatics↗

bioRxiv2025

Targeting RUNX1 in Macrophages Facilitates Cardiac Recovery

Despite advances in disease treatment, our understanding of how damaged organs recover and the mechanisms governing this process remain poorly defined. Here, we mapped the transcriptional and regulatory landscape of human cardiac recovery using single cell multiomics. Macrophages emerged as the most reprogrammed cell type. Deep learning identified the transcription factor RUNX1 as a key regulator of this process. Macrophage-specific Runx1 deletion recapitulated the human cardiac recovery phenotype in a chronic heart failure model. Runx1 deletion reprogrammed macrophages to a reparative phenotype, reduced fibrosis, and promoted cardiomyocyte adaptation. RUNX1 chromatin profiling revealed a conserved regulon that diminished during recovery. Mechanistically, the epigenetic reader BRD4 controlled Runx1 expression in macrophages. Chromatin activity mapping, combined with CRISPR perturbations, identified the precise regulatory element governing Runx1 expression. Therapeutically, small molecule Runx1 inhibition was sufficient to promote cardiac recovery. Our findings uncover a druggable RUNX1 epigenetic mechanism that orchestrates recovery of heart function.

immunology↗

bioRxiv2025

A Novel FNDC1-NAMPT-NAD axis is Implicated in Small and Large-vessel Arterial Disease and Drives Vascular Calcification

Vascular calcification represents a convergent pathological feature of diverse cardiovascular diseases, yet the upstream molecular programs orchestrating this process remain poorly defined. Here, we uncover fibronectin type III domain-containing 1 (FNDC1) as a previously unrecognized regulator of vascular calcification across both microvascular and macrovascular beds. Integrative transcriptomic profiling of human calciphylaxis lesions and atherosclerotic coronaries identified FNDC1 as one of the most significantly upregulated genes. In primary human vascular smooth muscle cells, FNDC1 drove osteogenic phenotype switch and vascular calcification through activation of PI3K/AKT signaling and metabolic reprogramming. Mechanistically, FNDC1 directly binds to nicotinamide phosphoribosyltransferase (NAMPT) resulting in elevated intracellular NAD levels, thus coupling vascular signaling to control of NAD biosynthesis. In murine models, genetic deletion of Fndc1 or pharmacologic inhibition of NAMPT suppressed arterial calcification and prolonged survival. Clinically, circulating FNDC1 levels were elevated in patients with both calciphylaxis and coronary artery disease and independently predicted cardiovascular risk in 42,687 UK Biobank participants. Together, these findings establish FNDC1 as a central mediator of vascular pathology and highlight the FNDC1- NAMPT-NAD+ axis as a promising target for therapeutic intervention.

cell biology↗

bioRxiv2025

Targeting Modulated Vascular Smooth Muscle Cells in Atherosclerosis via FAP-Directed Immunotherapy

Vascular smooth muscle cell (VSMC) and immune cell diversification play a central role in driving atherosclerotic coronary artery disease (CAD)1-3. However, the molecular mechanisms governing cell state transitions within the neo-intima in human CAD remain poorly understood, and no lipid-independent therapies are currently approved for its treatment. Here, we performed multi-omic single-cell gene expression profiling, epitope mapping, and spatial transcriptomics from 27 human coronary arteries. Our analysis identified fibroblast activation protein (FAP) as a marker of modulated VSMCs within the neo-intima. Genetic lineage tracing in mice confirmed that FAP cells in the plaque originate from medial VSMCs. Additionally, non-invasive positron emission tomography (PET) imaging in patients with CAD revealed focal FAP uptake in atherosclerotic lesions. Spatial transcriptomics further delineated the distinct localization of VSMC and immune cell subsets within plaques, with FAP states enriched in the neo-intima. To explore the therapeutic potential of targeting de-differentiated VSMCs, we developed an anti-FAP bispecific T-cell engager (BiTE) and demonstrated that it significantly reduced the plaque burden in multiple mouse models of atherosclerosis. Collectively, our study provides the first single-cell and spatially resolved map of human CAD, establishes FAP as a marker of modulated smooth muscle cells, and demonstrates the broader potential of immunotherapeutics for lipid independent targets in atherosclerotic CAD.

genomics↗

bioRxiv2025

Wnt-dependent spatiotemporal reprogramming ofbone marrow niches drives fibrosis

Bone marrow fibrosis is the most extensive matrix remodeling of the microenvironment and can include de novo formation of bone (osteosclerosis). Spatiotemporal information on the contribution of distinct bone marrow niche populations to this process is incomplete. We demonstrate that fibrosis-inducing hematopoietic cells cause profibrotic reprogramming of perivascular CXCL12 abundant reticular (CAR) progenitor cells resulting in loss of their hematopoiesis-support and upregulation of osteogenic and pro-apoptotic programs. In turn, peritrabecular osteolineage cells (OLCs) are activated in an injury-specific, Wnt-dependent manner, comparable to skeletal repair. OLCs fuel bone marrow fibrosis through their expansion and skewed differentiation, resulting in osteosclerosis and expansion of Ly6a+ fibroblasts. NCAM1 expression marks peritrabecular OLCs and their expansion into the central marrow is specific for fibrosis in mice and patients. Peritrabecular stromal b-catenin expression is linked to fibrosis in patients and inhibition of Wnt signaling reduces bone marrow fibrosis and osteosclerosis, possibly being a clinically relevant therapeutic target.

molecular biology↗

bioRxiv2025

SwarmMAP: Swarm Learning for Decentralized Cell Type Annotation in Single Cell Sequencing Data

Rapid technological advancements have made it possible to generate single-cell data at a large scale. Several laboratories around the world can now generate single-cell transcriptomic data from different tissues. Unsupervised clustering, followed by annotation of the cell type of the identified clusters, is a crucial step in single-cell analyses. However, there is no consensus on the marker genes to use for annotation, and celltype annotation is currently mostly done by manual inspection of marker genes, which is irreproducible, and poorly scalable. Additionally, patient-privacy is also a critical issue with human datasets. There is a critical need to standardize and automate celltype annotation across datasets in a privacy-preserving manner. Here, we developed SwarmMAP that uses Swarm Learning to train machine learning models for cell-type classification based on single-cell sequencing data in a decentralized way. SwarmMAP does not require any exchange of raw data between data centers. SwarmMAP has a F1-score of 0.93, 0.98, and 0.88 for cell type classification in human heart, lung, and breast datasets, respectively. Swarm Learning-based models yield an average performance of 0.907 which is on par with the performance achieved by models trained on centralized data (p-val=0.937, Mann-Whitney U Test). We also find that increasing the number of datasets increases cell-type prediction accuracy and enables handling higher cell-type diversity. Together, these findings demonstrate that Swarm Learning is a viable approach to automate cell-type annotation. SwarmMAP is available at https://github.com/hayatlab/SwarmMAP.

bioinformatics↗

bioRxiv2024

mcBERT: Patient-Level Single-cell Transcriptomics Data Representation

Single-cell RNA sequencing (scRNA-seq) transcriptomics improves our understanding of cellular heterogeneity in healthy and pathological states. However, most scRNA-seq analyses remain confined to single cells or distinct cell populations, limiting their clinical applicability. Addressing the need to translate single-cell insights into a patient-level disease understanding, we introduce mcBERT, a new method that leverages scRNA-seq data and a transformer-based model to generate integrative patient representations using a self-supervised learning phase followed by contrastive learning to refine these representations. Our evaluations of mcBERT across 7 million cells from 1223 individuals encompassing diverse disease states in heart, kidney, blood cell, and lung tissues show that learned representations facilitate a robust identification of disease cohorts and enable comparisons of patient similarity. Moreover, our findings indicate that mcBERT can accurately classify disease phenotypes, also in previously unseen biospecimens and patients. Independent of the specific tissue, mcBERT extends the utility of scRNA-seq data from cellular analysis to potentially actionable patient-centric applications.

bioinformatics↗

bioRxiv2024

Dynamic multi-omics and mechanistic modeling approach uncovers novel mechanisms of kidney fibrosis progression

Kidney fibrosis, characterized by excessive extracellular matrix deposition, is a progressive disease that, despite affecting 10% of the population, lacks specific treatments and suitable biomarkers. This study presents a comprehensive, time-resolved multi-omics analysis of kidney fibrosis using an in vitro model system based on human kidney PDGFR{beta}+ mesenchymal cells aimed at unraveling disease mechanisms. Using transcriptomics, proteomics, phosphoproteomics, and secretomics we quantified over 14,000 biomolecules across seven time points following TGF-{beta} stimulation. This revealed distinct temporal patterns in the expression and activity of known and potential kidney fibrosis markers and modulators. Data integration resulted in time-resolved multi-omic network models which allowed us to propose mechanisms related to fibrosis progression through early transcriptional reprogramming. Using siRNA knockdowns and phenotypic assays, we validated predictions and regulatory mechanisms underlying kidney fibrosis. In particular, we show that several early-activated transcription factors, including FLI1 and E2F1, act as negative regulators of collagen deposition and propose underlying molecular mechanisms. This work advances our understanding of the pathogenesis of kidney fibrosis and provides a resource to be further leveraged by the community. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=71 SRC="FIGDIR/small/618507v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@d2bfaborg.highwire.dtl.DTLVardef@257d34org.highwire.dtl.DTLVardef@13f06f3org.highwire.dtl.DTLVardef@e16e6c_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

systems biology↗

bioRxiv2024

PHLOWER - Single cell trajectory analysis using Decomposition of the Hodge Laplacian

Multi-modal single-cell sequencing, which captures changes in chromatin and gene expression in the same cells, is a game changer in the study of gene regulation in cellular differentiation processes. Computational trajectory analysis is a key computational task for inferring differentiation trees from this single-cell data, though current methods struggle with complex, multi-branching trees and multi-modal data. To address this, PHLOWER (decomPosition of the Hodge Laplacian for inferring trajectOries from floWs of cEll diffeRentiation) leverages the harmonic component of the Hodge decomposition on simplicial complexes to infer trajectory embeddings. These natural representations of cell differentiation facilitate the estimation of their underlying differentiation trees. We evaluate PHLOWER through benchmarking with multi-branching differentiation trees and using novel kidney organoid multi-modal and spatial single-cell data. These demonstrate the power of PHLOWER in both the inference of complex trees and the identification of transcription factors regulating off-target cells in kidney organoids.

bioinformatics↗

bioRxiv2024

PICASO: Profiling Integrative Communities of Aggregated Single-cell Omics data

Various single-cell modalities covering transcriptomics, epigenetic and spatio-temporal changes in health and disease phenotypes are used in an exploratory way to understand biological systems at single-cell resolution. However, the vast amount of such single-cell data is not systematically linked to existing biomedical data. Networks have previously been used to represent harmonized biomedical data. Integrating various resources of biomedical data in networks has recently received increasing attention. These aggregated networks can provide additional insight into the biology of complex human diseases at cell-type level, however, lack inclusion of single cell expression data. Here, we present the PICASO framework, which incorporates single-cell gene expression data as an additional layer to represent associations between cell types, disease phenotypes, drugs and genes. The PICASO network includes several standardized biomedical databases such as STRING, Uniprot, GeneOntology, Reactome, OmniPath and OpenTargets. Using multiple cell type-specific instances of the framework, each annotated and scored with their respective expression data, comparisons between disease states can be made by computing respective sub-networks and comparing the expression scores between conditions. Ultimately, these group-specific networks will allow the identification of relevant genes, processes and potentially druggable targets, as well as the comparison of different measured groups and thus the identification of group-specific communities and interactions.

bioinformatics↗

bioRxiv2024

scDecorr - Feature decorrelation representation learning with domain adaptation enables self-supervised alignment of multiple single-cell experiments

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular het-erogeneity in complex biological systems. However, analyzing and integrating scRNA-seq data poses unique computational challenges due to sparsity, high variability, and technical batch effects. Here, we propose a novel framework called scDecorr for robust representation learning and data integration for scRNA-seq analysis. Our approach leverages the idea of feature decorrelation-based self-supervised learning (SSL) to obtain efficient low-dimensional representations of individual cells without relying on negative samples. By maximizing similarity among distorted embeddings while decorrelating their components, scDecorr captures the biological signature while eliminating technical noise. Furthermore, scDecorr incorporates unsupervised domain adaptation to bridge the gap between batches with different probability distributions, enabling effective integration of scRNA-seq data from diverse sources. Our framework achieves domain-invariant representations by learning cell embeddings independently across domains and employing domain-specific batch normalization. We evaluate scDecorr on a variety of single cell datasets and demonstrate its ability to integrate batches without losing the inherent biological variance, thereby facilitating optimal clustering. The representations generated by scDecorr also exhibit robustness in label transfer tasks, allowing for effective transfer of cell-type labels from reference to query datasets. Overall, scDecorr offers a powerful tool for efficient analysis and integration of large and complex scRNA-seq datasets, advancing our understanding of cellular processes and disease mechanisms. The code is available here https://github.com/hayatlab/scdecorr.

bioinformatics↗

bioRxiv2024

Shift in cellular crosstalk reveals the immunosuppressive environment in cholangiocarcinoma patients.

Background and aimsCholangiocarcinoma (CCA) is a deadly disease, and this cancer entity is characterized by an abundant stroma. The tumor microenvironment (TME) plays an important role in aggressive behavior and poor response to therapeutics; however, underlying pathways are unknown. MethodsTo fill this gap, we used multiplexed immunohistochemistry, high-dimensional phenotyping, and transcriptomics to analyze human CCA samples and identify cell cluster crosstalk in patients with a poor prognosis. ResultsOur findings confirmed the presence of Tregs and the lack of effector memory cells in the tumor. New findings are the spatiality of the effector memory cells being more present in the peripheral tissue, for some reason these immune cells fail to reach the tumor niche. We revealed cancer crosstalk with macrophages and stromal cells and identified responsible genes in the poor prognosis group. Amongst the responsible ligand pairs are GAS6-AXL belonging to the TAM family. We then identified VCAN-TLR2 to be present and influencing the ECM in a way to supports immune exhaustion. Last, EGFR-TGF-{beta} is expressed in macrophages and this finding is important in Tregs induction and blocking cytotoxic T cell function. ConclusionsThe multiple mechanisms leading to the exclusion of relevant immune cells needed for an anti-cancer response and mechanisms leading to active immune suppression are part of complex cell-cell crosstalk. This study provides a deeper insight into the immune exhausted phenotype in CCA.

cancer biology↗