Biology subjects

Hense, J.

Publications and source records attributed to Hense, J..

7 recordsLinked to original sources

bioRxiv2026

Discovery of the first small-molecule extracellular inhibitor of KCa3.1

The ion channel KCa3.1 plays a role in immune regulation, red blood cell function, and is linked to numerous types of cancer. Various animal toxins, such as maurotoxin, bind to the extracellular side of KCa3.1, providing a potential starting point for inhibitor development. We report in this work the discovery of a novel, small-molecule inhibitor, with a micromolar IC50, which was specifically designed to target plasma-membrane KCa3.1 channels from the extracellular side. This compound can serve as a starting point for the development of more selective inhibitors and probes. For the identification of new extracellular inhibitors, molecular dynamics simulations were performed using the experimental structures of KCa3.1 and maurotoxin. The simulations produced a validated binding mode, highlighting key residues involved in the interaction between the toxin and the channel. These findings laid the foundation for the structure-based identification of novel extracellular small-molecule inhibitors of KCa3.1. The Molport database, containing approximately 50 million compounds, was screened using protein-ligand docking, yielding a hit molecule that was experimentally confirmed using patch clamp assays.

pharmacology and toxicology↗

bioRxiv2025

Digital Spatial Pathway Mapping Reveals Prognostic Tumor States in Head and Neck Cancer

Head and neck squamous cell carcinoma (HNSCC) is a morphologically and molecularly heterogeneous disease with limited effectiveness of genotype-informed therapies. Transcriptome-derived estimates of signaling pathway activity carry prognostic and therapeutic potential but remain inaccessible in routine diagnostics due to cost and tissue constraints. Here, we introduce Digital Spatial Pathway Mapping, an AI-based computational pathology framework that infers signaling pathway activities directly from routine hematoxylin and eosin (H&E) slides, enabling in-silico spatial molecular readouts from standard histology. Models trained on HPV-negative HNSCC from TCGA and externally validated on CPTAC robustly predicted transcriptome-derived activities in cancer-relevant signaling pathways. To achieve spatial interpretability, we applied layer-wise relevance propagation (LRP) to generate heatmaps that highlight positive versus negative evidence for pathway activation. These LRP heatmaps were technically validated by patch-flipping tests and biologically validated against pathway-relevant immunohistochemistry in an independent patient cohort. From these explanations, we derived a tumor area pathway activity score (TAPAS) quantifying the spatial fraction of activated tumor regions within a slide. Applied to a retrospective HNSCC cohort of 1,066 slides from 112 resection specimens, TAPAS captured intratumoral heterogeneity and revealed two biologically dis-tinct tumor states - an oncogenic growth phenotype with widespread pathway activation and a pathway quiescent phenotype associated with higher recurrence risk independent of clinicopathological variables. These findings establish Digital Spatial Pathway Mapping as a scalable, in-silico approach to recover systems-level molecular information from standard histopathology, enabling prognostic and mechanistically grounded patient stratification in head and neck cancer.

pathology↗

bioRxiv2024

EXOSOMES FROM CYCLIC MICE MODULATE LIVER TRANSCRIPTOME IN ESTROUPAUSE MICE INDEPENDENT OF AGE

BackgroundExosomes are extracellular vesicles secreted by cells that contain microRNAs (miRNAs). These miRNAs can induce changes in gene expression and function of recipient cells. In different cells exosome content can change with age and physiological state affecting tissues function and health. AimsTherefore, the aim of this study was to characterize the miRNA content and role of exosomes from cyclic female mice in the modulation of liver transcriptome in estropausal mice. Main MethodsTwo-month-old female mice were induced to estropause using 4-vinylcyclohexene diepoxide (VCD). At six months of age VCD-treated mice were divided in control group (VCD) and exosome treated group (VCD+EXO), which received 10 injections at 3-day intervals of exosomes extracted from serum of cyclic female mice (CTL). Key findingsExosome injection in estropausal mice had no effect on body mass, insulin sensitivity or organ weight. We observed ten miRNAs differentially regulated in serum exosomes of VCD compared to CTL mice. In the liver we observed 931 genes differentially expressed in VCD+EXO compared to VCD mice. Interestingly, eight pathways were up-regulated in liver by VCD treatment and down-regulated by exosome treatment, indicating that exosomes from cyclic mice can reverse changes promoted by estropause in liver. Cyp4a12a expression which is male-specific was increased in VCD females and not reversed by exosome treatment. SignificanceOur findings indicate that miRNAs content in exosomes is regulated by estropause in mice independent of age. Additionally, treatment of estropausal mice with exosomes from cyclic mice can partially reverse changes in liver transcriptome.

physiology↗

bioRxiv2024

Enhanced cardiac mitochondrial biogenesis by nitro-oleic acid remedies diastolic dysfunction in a mouse model of heart failure with preserved ejection fraction

Prevalence of heart failure with preserved ejection fraction (HFpEF) is increasing, while treatment options are inadequate. Hypertension and obesity-related metabolic dysfunctions contribute to HFpEF progression. Nitro-oleic acid (NO2-OA) impacts metabolic processes by improving glucose tolerance and adipocyte function. In this study, 4 week treatment with NO2-OA ameliorated diastolic dysfunction in a HFpEF mouse model induced by high-fat diet and inhibition of the endothelial nitric oxide synthase. A proteomic analysis of left ventricular tissue revealed, that one third of the identified proteins, mostly mitochondrial proteins, were upregulated in hearts of NO2-OA-treated HFpEF mice compared to controls and vehicle-treated HFpEF mice, which was confirmed by immunoblot. Activation of the 5-adenosine-monophosphate-activated-protein-kinase (AMPK) signaling pathway mediated an enhancement of mitochondrial biogenesis in hearts of NO2-OA-treated HFpEF mice. In cardiomyocytes under metabolic stress, NO2-OA increased mitochondrial protein level accompanied by enhanced oxidative phosphorylation. In conclusion, targeting mitochondrial integrity in HFpEF leads to improved diastolic function.

pathology↗

bioRxiv2023

A single-cell atlas of the aging murine ovary

Ovarian aging leads to diminished fertility, dysregulated endocrine signaling, and increased chronic disease burden. These effects begin to emerge long before follicular exhaustion. Around 35 years old, women experience a sharp decline in fertility, corresponding to declines in oocyte quality. Despite a growing body of work, the field lacks a comprehensive cellular map of the transcriptomic changes in the aging ovary to identify early drivers of ovarian decline. To fill this gap, we performed single-cell RNA sequencing on ovarian tissue from young (3-month-old) and reproductively aged (9-month-old) mice. Our analysis revealed a doubling of immune cells in the aged ovary, with lymphocyte proportions increasing the most, which was confirmed by flow cytometry. We also found an age-related downregulation of collagenase pathways in stromal fibroblasts, which corresponds to rises in ovarian fibrosis. Follicular cells displayed stress response, immunogenic, and fibrotic signaling pathway inductions with aging. This report raises provides critical insights into mechanisms responsible for ovarian aging phenotypes.

cell biology↗

bioRxiv2022

Large-scale systematic feasibility study on the pan-cancer predictability of multi-omic biomarkers from whole slide images with deep learning

We assessed the pan-cancer predictability of multi-omic biomarkers from haematoxylin and eosin (H&E)-stained whole slide images (WSI) using deep learning (DL) throughout a systematic study. A total of 13,443 DL models predicting 4,481 multi-omic biomarkers across 32 cancer types were trained and validated. The investigated biomarkers included a broad range of genetic, transcriptomic, proteomic, and metabolic alterations, as well as established markers relevant for prognosis, molecular subtypes and clinical outcomes. Overall, we found that DL can predict multi-omic biomarkers directly from routine histology images across solid cancer types, with 50% of the models performing at an area under the curve (AUC) of more than 0.633 (with 25% of the models having an AUC larger than 0.711). A wide range of biomarkers were detectable from routine histology images across all investigated cancer types, with a mean AUC of at least 0.62 in almost all malignancies. Strikingly, we observed that biomarker predictability was mostly consistent and not dependent on sample size and class ratio, suggesting a degree of true predictability inherent in histomorphology. Together, the results of our study show the potential of DL to predict a multitude of biomarkers across the omics spectrum using only routine slides. This paves the way for accelerating diagnosis and developing more precise treatments for cancer patients.

cancer biology↗

bioRxiv2022

Evaluation of a predictive method for the H&E-based molecular profiling of breast cancer with deep learning

We present a public validation of PANProfiler (ER, PR, HER2), an in-vitro medical device (IVD) that predicts the qualitative status of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) by analysing the hematoxylin and eosin (H&E)-stained tissue scan. In public validation on 648 (ER), 648 (PR) and 560 (HER2) unseen cases with known biomarker status, the device achieves an accuracy of 87% (ER), 83% (PR) and 87% (HER2). The validation offers early evidence of the ability to predict clinically relevant breast biomarkers from an H&E slide in a relevant clinical setting.

cancer biology↗