Biology subjects

Oefner, P. J.

Publications and source records attributed to Oefner, P. J..

5 recordsLinked to original sources

bioRxiv2026

Glutamine and GLS1 are key metabolic checkpoints that Limit T cell Infiltration and Function in HNSCC

Head and neck squamous cell carcinoma (HNSCC) is characterized by poor survival and limited benefit from immune checkpoint blockade (ICB), underscoring the need to define targetable mechanisms of immune escape. In a prospective, patient-centered study, we investigated how tumor metabolic reprogramming impairs T cell function and drives ICB resistance. Targeted metabolomics identified two dominant and distinct tumor states: the Warburg phenotype and glutamine deprivation and revealed increased tumor-specific glutamine to glutamate turnover. Integrating spatial metabolic imaging with immunohistochemistry linked these states to immune-cold and immune-excluded tumor architectures. Metabolic states selectively reduced the expression of T cell chemokine receptors highly abundant and prognostic in HNSCC. Accordingly, T cell migration into tumor spheroids was reduced at lower glutamine concentrations. Interfering with glutamine metabolism by pharmacologic inhibition or genetic deletion of glutaminase-1 markedly increased tumor susceptibility to immune cell-mediated cytotoxicity in vitro and ex vivo and sensitized patient-derived tumor fragments (PDTFs) to ICB. This effect extended beyond glutamine restoration and involved disruption of glutathione-dependent ROS defense, impaired tumor proliferation, and increased MHC expression in tumor cells. Consistently, high expression of genes involved in glutamine and glutathione metabolism associated with reduced ICB response in patients. In contrast to HNSCC, other tumor entities including breast cancer show low glutaminase-1 expression. Accordingly, breast cancer PDTFs were not responsive to glutaminase-1 inhibition. Together, these data identify glutaminase-1 as a metabolic checkpoint driving immune escape in HNSCC, and support guided patient stratification for metabolism-targeted immunotherapy, similar to current precision approaches for mutation targeted drugs.

cancer biology↗

bioRxiv2026

DESI-MS-Based Analysis of Drug Distribution in Human Renal Cystic Tissue Using the Chorioallantoic Membrane (CAM) as a 3D In Vivo Model

The chorioallantoic membrane (CAM) model represents a promising three-dimensional in vivo platform for preclinical drug testing in human tissues. In this study, we investigated whether the tissue penetration and distribution of benzbromarone, a known inhibitor of the Ca2+ activated chloride channel TMEM16A and potential therapeutic agent for autosomal dominant polycystic kidney disease (ADPKD), can be successfully visualized in human renal cyst tissue cultured on the CAM. To this end, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) combined with an ultrahigh-resolution time-of-flight mass spectrometer was employed. We achieved spatially resolved molecular mapping of endogenous metabolites and lipids as well as the applied compound. MSI enabled clear differentiation between CAM and cystic tissue based on their distinct lipid profiles. Benzbromarone was reproducibly detected in the cyst specimens and exhibited selective accumulation along the cyst epithelium, which is considered the principal site of action. These observations were complemented by multivariate analyses including Uniform Manifold Approximation and Projection (UMAP), and sparse multinomial logistic zero-sum classification. The data-driven approach confirmed molecular differences between tissue types and allowed accurate classification of drug-treated and untreated regions. This study demonstrates that topically applied benzbromarone penetrates human renal cyst tissue in the CAM model and localizes to pharmacologically relevant tissue regions, notably the location of the Ca2+ activated chloride channel TMEM16A in the epithelial lining. The integration of high-resolution DESI-MSI with advanced statistical analysis provides a robust and label-free method to study drug distribution in human tissue grafts. Our findings contribute to the advancement of translational research in analytical chemistry and pharmacology.

biochemistry↗

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↗

bioRxiv2023

PriOmics: integration of high-throughput proteomic data with complementary omics layers using mixed graphical modeling with group priors

Mass spectrometry (MS)-based high-throughput proteomics data cover abundances of 1,000s of proteins and facilitate the study of co- and post-translational modifications (CTMs/PTMs) such as acetylation, ubiquitination, and phosphorylation. Yet, it remains an open question how to holistically explore such data and their relationship to complementary omics layers or phenotypical information. Network inference methods aim for a holistic analysis of data to reveal relationships between molecular variables and to resolve underlying regulatory mechanisms. Among those, graphical models have received increased attention as they can distinguish direct from indirect relationships, aside from their generalizability to diverse data types. We propose PriOmics as a graphical modeling approach to integrate proteomics data with complementary omics layers and pheno- and genotypical information. PriOmics models intensities of individual peptides and incorporates their protein affiliation as prior knowledge in order to resolve statistical relationships between proteins and CTMs/PTMs. We show in simulation studies that PriOmics improves the recovery of statistical associations compared to the state of the art and demonstrate that it can disentangle regulatory effects of protein modifications from those of respective protein abundances. These findings are substantiated in a dataset of Diffuse Large B-Cell Lymphomas (DLBCLs) where we integrate SWATH-MS-based proteomics data with transcriptomic and phenotypic information. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/566517v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@19bcac0org.highwire.dtl.DTLVardef@11c126forg.highwire.dtl.DTLVardef@1fe6631org.highwire.dtl.DTLVardef@e73187_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2022

SpaCeNet: Spatial Cellular Networks from omics data

Advances in omics technologies have allowed spatially resolved molecular profiling of single cells, providing a window not only into the diversity and distribution of cell types within a tissue, but also into the effects of interactions between cells in shaping the transcriptional landscape. Cells send chemical and mechanical signals which are received by other cells, where they can subsequently initiate context-specific gene regulatory responses. These interactions and their responses shape the individual molecular phenotype of a cell in a given microenvironment. RNAs or proteins measured in individual cells together with the cells spatial distribution provide invaluable information about these mechanisms and the regulation of genes beyond processes occurring independently in each individual cell. "SpaCeNet" is a method designed to elucidate both the intracellular molecular networks (how molecular variables affect each other within the cell) and the intercellular molecular networks (how cells affect molecular variables in their neighbors). This is achieved by estimating conditional independence relations between captured variables within individual cells and by disentangling these from conditional independence relations between variables of different cells. A python implementation of SpaCeNet is publicly available at https://github.com/sschrod/SpaCeNet.

bioinformatics↗