Biology subjects

Eckstein, M.

Publications and source records attributed to Eckstein, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Spatial proteomic profiling of cardiac stromal populations and microenvironments in systemic sclerosis-associated primary myocardial involvement

Objectives: To define cardiac stromal heterogeneity and spatial stromal-immune organization in systemic sclerosis-associated virus-negative myocarditis (SSc-VNM), a manifestation of primary SSc heart involvement. Methods: Imaging mass cytometry, a single-cell multiplexed spatial proteomic technology, was applied to endomyocardial biopsies from ten patients with SSc-VNM, seven with VNM associated with other systemic autoimmune rheumatic diseases (a-VNM) and nine with isolated VNM (i-VNM). Single-cell phenotyping, cellular-neighborhood and spatial-interaction analyses, and correlations with histological fibrosis were performed at patient level. Results: Among 82,673 segmented cells, we identified seven fibroblast phenotypes and one CD34+ stromal-cell population. The overall fibroblast/stromal compartment was expanded in SSc-VNM compared with i-VNM (adjusted p = 0.00973). The composition of fibroblasts/stromal cells differed profoundly in SSc-VNM compared with a-VNM and i-VNM, with numerical increases in CD34+ stromal cells and SOX9+ and PI16high/POSTNhigh fibroblasts and de-enrichment of FAPhigh/POSTNhigh fibroblasts and myofibroblasts. Spatial analysis identified ten cellular neighborhoods and revealed distinct SSc-VNM-associated patterns, including altered stromal-immune interaction networks. These included enriched interactions of CD34+ stromal cells and PI16high/POSTNhigh fibroblasts with CD163high/HLA-DR+ macrophages and CD68+ mast cells. PI16high/POSTNhigh and PI16-/POSTNhigh fibroblasts demonstrated the highest collagen type 1 expression among all stromal cell subsets across all VNM entities and PI16high/POSTNhigh fibroblasts were numerically increased in biopsies with [≥]10% fibrotic remodeling and were spatially associated with collagen-rich areas. Conclusions: Our findings refine current concepts of human fibrotic myocardial remodeling and support therapeutic targeting of specific stromal players such as POSTNhigh fibroblast subpopulations. Keywords: Systemic Sclerosis; Myocarditis; Myocardial Fibrosis; Fibroblasts; Imaging Mass Cytometry.

bioinformatics↗

bioRxiv2026

Spatial organization of fibroblast subpopulations and niches across acute repair and chronic fibrotic remodeling of the human myocardium

Myocardial fibrosis is a common end point of acute and chronic cardiovascular injury, yet the spatially organized fibroblast (Fb) and immune-cell states that sustain fibrotic remodeling across etiologies remain incompletely defined. We applied cyclic in situ hybridization (cISH)-based spatial transcriptomics to myocardial biopsies from patients with acute myocardial infarction (AMI), ischemic cardiomyopathy (ICM), hypertrophic cardiomyopathy (HCM), postviral myocardial fibrosis (PVMF), and systemic sclerosis associated primary heart involvement (SSc-pHI). CODEX-based protein imaging provided orthogonal support for cell subpopulations and cellular neighborhoods. Spatially informed clustering identified six Fb subpopulations: COL1A1hi/POSTNhi, NOTCH3+, TIMP1hi, GSNhi/DCNhi, AP1hi Fbs, and myofibroblasts. COL1A1hi/POSTNhi and NOTCH3+ Fbs were enriched in AMI, whereas GSNhi/DCNhi Fbs and myofibroblasts predominated in chronic fibrotic remodeling. These subpopulations displayed distinct extracellular matrix, inflammatory, and vasoregulatory programs. Infarction-associated reparative niches were enriched in COL1A1hi/POSTNhi or NOTCH3+ Fbs and SPP1+ or APOE+ macrophages (M{Phi}), whereas chronic fibroinflammatory niches were enriched in GSNhi/DCNhi or AP1hi Fbs or myofibroblasts with C1Q+ M{Phi} and plasma cells. The GSNhi/DCNhi Fb niche marked areas of subendocardial fibrosis, whereas the AP1hi Fb niche localized to perivascular fibrosis. In SSc-pHI, the actively fibrogenic COL1A1hi/POSTNhi Fb state characteristic of infarct scar formation was present at higher frequency than in other chronic entities and was individually associated with cardiac dysfunction, whereas the chronic GSNhi/DCNhi Fb niche was associated with the extent of interstitial fibrosis and anti-topoisomerase I autoantibodies. These data show that human myocardial fibrosis is organized into spatially distinct Fb subpopulations that assemble into context-specific fibroinflammatory niches across chronic fibrotic remodeling and repair after AMI.

cell biology↗

bioRxiv2026

A neuroimmune IL-13 axis is associated with human enteric nervous system development

The mechanisms governing maturation of the human enteric nervous system (ENS) during early postnatal life remain poorly defined. Here, we characterize the transcriptomic, cellular, and functional landscape of the neonatal human ileum and identify a neuro-immune axis associated with ENS expansion. Using human tissue transcriptomics, flow cytometry, iPSC-derived enteric neural lineages, and single-cell interactome analyses, we show that the neonatal ileum is enriched for pro-neurogenic transcriptional programs and harbors a greater abundance of enteric neurons and glia than the adult tissue. T cells emerge as a predominant source of interleukin-13 (IL-13) in the neonatal gut, and enteric neurons express its receptor IL13RA1, enabling direct immune-to-neuron signaling. Functional experiments demonstrate that IL-13 enhances expression of key enteric neuronal markers in a concentration-dependent manner. In parallel, single-cell analyses identify enteric neurons as a major predicted source of macrophage migration inhibitory factor (MIF), with signaling directed toward T and NK cell populations, suggesting that the ENS actively shapes the immune environment it depends upon. Together, these findings support a model in which bidirectional neuro-immune communication establishes a pro-neurogenic niche during a critical window of ENS development. This work positions the neonatal immune system as an active contributor to ENS maturation and offers a new perspective on how neuro-immune crosstalk shapes intestinal development in early life.

neuroscience↗

bioRxiv2026

Nerve growth factor receptor identifies a basal subpopulation linked to poor prognosis and reduced immunotherapy responses in bladder cancer

PurposeBladder cancer (BLCA) is a heterogeneous tumor type. Only one third of muscle-invasive (MIBC) patients respond to immune checkpoint inhibitors (ICIs). Reliable resistance markers are needed to guide clinical decisions. We investigated the nerve growth factor receptor (NGFR) in BLCA and analyzed its correlation with disease progression and response to immunotherapy. Experimental DesignWe analyzed NGFR expression in BLCA cell lines, organoids, mouse models and patient samples. The cohorts used were The Cancer Genome Atlas (TCGA), enriched in muscle-invasive bladder cancer (MIBC) (n=407); IMvigor210, representing MIBC patients treated with ICIs (n=348); and UROMOL2, as a non-muscle-invasive bladder cancer (NMIBC)-specific cohort (n=535). IMvigor010 was also included (n=728). Patients were stratified by NGFR expression quartiles. We analyzed survival and tumor subtypes and performed stromal deconvolution and functional profiling. We assessed stemness- and invasion-related features in SCaBER cells. ResultsNGFR marks a basal tumor cell subcluster and is independently associated with poor prognosis in TCGA and IMvigor210. NGFR-high tumors show stromal content enriched in cancer-associated fibroblasts, lower neoantigen burden, higher CD8+ T effector signature together with an immune-excluded phenotype, and a CAF-specific TGF{beta} signature. In the immunotherapy-treated cohort, high NGFR expression was also associated with poorer outcome. Functionally, NGFR appears to promote a stem-like/pro-invasive program in BLCA cells. ConclusionsNGFR identifies a basal-like BLCA subpopulation linked to poor survival, while its association with immunotherapy response requires further validation. In addition, our in vitro analyses support a role of NGFR in stem-like and invasive traits, highlighting its relevance as a biomarker in BLCA.

cancer biology↗

bioRxiv2026

Identification of non-covalent inhibitors for the atypical peroxiredoxin PRDX5 as a therapeutic strategy in malignant pleural mesothelioma

Malignant pleural mesothelioma (MPM) is an aggressive asbestos-linked cancer with limited therapeutic options and a dismal 5-year survival rate of [~]5%. While aberrant production of reactive oxygen and nitrogen species (ROS/RNS) is a hallmark of MPM, targeted approaches to exploit these redox vulnerabilities remain scarce. Here, using the MOSAIC multimodal cancer patient atlas, we identify Peroxiredoxin 5 (PRDX5) as being significantly upregulated in the epithelioid subtype of MPM. We show that MPM cells exhibit enhanced resistance to nitrosative and oxidative stress compared to healthy mesothelial cells, a phenotype correlated with basal PRDX5 expression. Next, utilising a machine learning guided discovery pipeline, we identified three putative allosteric pockets in PRDX5 and conducted a virtual screen of 3.6 million compounds. High-throughput biochemical validation of 452 candidates yielded 36 non-covalent hits, including sub-micromolar inhibitors. These findings establish PRDX5 as a novel, subtype specific therapeutic target in MPM and provide a chemical framework for the development of next-generation redox-modulating oncology treatments.

cancer biology↗

bioRxiv2026

Pan-cancer virtual spatial transcriptomics from routine histology with Phoenix

Spatial transcriptomics links gene expression to tissue architecture, providing a mechanistic view of cellular organization. Yet existing datasets cover few donors and miss the complexity of human disease. Experimental costs remain prohibitive, and large-scale profiling is impractically slow for population-level studies. Accurate computational methods are urgently needed. Predicting gene expression from standard histology, however, remains an open problem, as current approaches transfer poorly to unseen cohorts and diseases. Here, we present Phoenix, a (latent) flow matching generative model that infers pan-cancer spatially resolved single-cell gene expression with high accuracy. Phoenix analyzes treatment response in silico: Applied to 763 head and neck cancer patients, it identified three new spatial biomarkers that we validated across two cancers (breast cancer, n = 84; ovarian cancer, n = 157) and treatment regimens (platinum, trastuzumab). Phoenix generalizes beyond carcinomas: In a large sarcoma cohort (802 tissue microarray cores), it accurately predicted cell-type-specific signatures in held-out samples and captured chemotherapy-induced immune remodeling. Phoenix also extends across species: In a mouse model, it accurately predicted the expression of pancreatic cancer lineage markers and the mutant mKras^G12D allele in silico. In total, we evaluated Phoenix on over 10,000 patients. Our results establish virtual spatial transcriptomics as a scalable framework for studying tissue organization, therapeutic response, and disease mechanisms.

bioinformatics↗

bioRxiv2026

The Glucose Transporter GLUT3 Controls Regulatory T Cell Function

Regulatory T (Treg) cells are central mediators of immune tolerance and are generally considered to rely predominantly on mitochondrial metabolism rather than glucose-driven glycolysis. To define the role of glucose metabolism in Treg cells, we investigated the contribution of the hexose transporters GLUT1 and GLUT3. Genetic ablation of GLUT1 in T cells or selectively in Treg cells had minimal impact on Treg cell numbers, phenotype or immune homeostasis, indicating that GLUT1 is largely dispensable in this lineage. By contrast, deletion of GLUT3 in T cells resulted in a marked reduction in Treg cell numbers. However, it remained unclear whether this reduction reflected diminished IL-2 production by GLUT3-deficient conventional T cells or a cell-intrinsic requirement for GLUT3 in Treg cells. To investigate this, we generated mice with Treg cell-specific deletion of GLUT3. These animals developed severe systemic inflammation accompanied by lethal cellular and humoral autoimmunity. Mechanistically, GLUT3-deficient Treg cells exhibited reduced glycolytic activity and mitochondrial respiration, leading to impaired suppressive function and defective effector and follicular Treg cell differentiation. Collectively, our findings demonstrate a non-redundant requirement for GLUT3 in Treg cell metabolic fitness and immune regulation, refining the prevailing view that Treg cells operate largely independently of glucose metabolism. Our data further suggest that therapeutic strategies targeting glucose uptake and glycolysis in autoimmune and inflammatory diseases should account for potential adverse effects on Treg cell-mediated immune tolerance.

immunology↗

bioRxiv2026

Multi-modal benchmarking of the Ultima UG100 and Illumina NovaSeq sequencing platforms using clinically relevant FFPE tissues

Emerging high-throughput sequencing technologies promise lower costs and higher scalability, yet their performance on archival clinical samples remains poorly characterized. Here, we benchmarked Ultima Genomics UG100 against Illumina Novaseq platforms across single-nuclei RNA-seq (snRNA-seq), whole-transcriptome (WTS), whole-exome (WES), and whole-genome sequencing (WGS) using FFPE tissues from oncologic and immune-mediated diseases. Across matched samples, we systematically assessed data quality, coverage profiles, error spectra, variant concordance and transcriptomic reproducibility. UG100 produced highly comparable results to Illumina, capturing key oncogenic and immune-related transcripts, accurately resolving cellular composition in snRNA-seq, and maintaining sensitivity for lowly expressed genes, despite characteristic insertion-biased indels and modest differences in multi-mapping reads. Discrepancies were subtle, largely limited to pseudogene and non-coding transcripts, and did not affect pathway-level conclusions. Ultima UG100 platform prioritized high precision and reduced low-frequency artifacts, offering a cleaner but more conservative variant-calling profile compared to the more sensitive, yet noisier, Illumina/DRAGEN workflow. This multimodal, clinically oriented assessment provides the first comprehensive evaluation of UG100, demonstrating its translational utility in population-scale genomics, and highlighting the potential for emerging sequencing technologies to lower the cost of biomedical research and clinical diagnostics.

genomics↗

bioRxiv2026

Polyploid cardiomyocytes define disease-specific transcriptional states in the mammalian heart

The adult mammalian heart has a limited regenerative capacity. Following injury, cardiomyocytes undergo a hypertrophic response accompanied by polyploidization, which has been described as a barrier to proliferation and regeneration of the heart1,2. However, the unique molecular programs of polyploidy, or genome multiplied cardiomyocytes, and their influence on the disease-related myocardial remodelling process remains unclear. Here, we integrate single-nuclei and high-resolution spatial multi-omics across human, rat, and mouse hearts to define novel cardiac cell states and their tissue niches in ischemic and non-ischemic heart disease. Computational analysis across scales allowed us to generate detailed networks of the cardiac tissue remodelling process as well as tissue and sub-cellular environments uniquely enriched in polyploid cardiomyocytes or their diploid origins. We identify a conserved, dichotomous transcriptional program distinguishing diploid from polyploid cardiomyocytes. Polyploid cardiomyocytes demonstrated rewired metabolic and chromatin-remodeling transcriptional programs and recapitulate the gene signature of immature human fetal cardiomyocytes. Notably, we observe that polyploid cardiomyocytes--rather than the general myocyte population--are the primary sites of enrichment for major heart-failure drug targets, including the mineralocorticoid, {beta}1-adrenergic, and glucagon-like peptide-1 receptors. Based on our cross-species dataset we further identified TNIK, a Wnt-pathway regulator expressed in polyploid cardiomyocytes across species, as a potential therapeutic target and demonstrate that pharmacological TNIK inhibition improves cardiac function after myocardial infarction in rats. Together, this species-spanning, disease-resolved study redefines cardiomyocyte heterogeneity in heart disease and suggests a therapeutic path to heart failure treatment by targeting polyploid cardiomyocytes.

genomics↗

bioRxiv2025

A computational SOX10 network-based selection strategy to identify new drug targets in uveal melanoma

Uveal melanoma (UM) is the most common intraocular malignancy in adults. In contrast to cutaneous melanoma (CM), effective treatment options for metastatic UM are limited. The transcription factor SOX10 is crucial for CM initiation and survival, making it an interesting candidate for new targeted therapies, but its relevance in UM was unclear. We found that SOX10 was widely expressed in UM and essential for proliferation, cell cycle progression, and survival. The effects were partially mediated by SOX10-related genes including MITF, highlighting high addiction of UM to the SOX10-MITF axis. Additionally, SOX10 knockdown induced massive transcriptomic changes. Due to a lack of specific inhibitors of SOX10 and MITF, a computational approach was used to identify druggable targets by curating a UM-specific protein interaction network to search for candidates downregulated upon SOX10 inhibition. Thereby, the E2F transcription factor family was identified and their potential as druggable target candidates in UM was confirmed using the pan-E2F inhibitor HLM006474, resulting in cell cycle arrest and apoptosis. Taken together, SOX10 is crucial for UM survival and SOX10-associated proteins may serve as promising targets for developing new therapeutic strategies in UM.

cancer biology↗

bioRxiv2025

Histology and spatial transcriptomic integration revealed infiltration zone with specific cell composition as a prognostic hotspot in glioblastoma

BackgroundGlioblastoma (GBM), the most aggressive primary brain tumor, has a median survival of approximately 15 months. Twenty percent of patients survive beyond three years, but known clinical factors like age, performance status, resection extent, and MGMT promoter methylation status do not fully explain the observed outcomes. ObjectiveOur objective was to identify novel histology derived biomarkers associated with end-of-spectrum overall survival (OS) to provide novel biological insight with a translational potential. MethodsWe analyzed a total of 748 GBM patients from 3 different cohorts, uniquely enriched in long survivors (n=98 with overall survival (OS) > 5y including n=196 with OS[≥]3y), with clinical data and H&E slides obtained from the primary tumor at baseline. We propose an interpretable machine learning (ML) methodology for the discovery of histological biomarkers. Our method learned to segment each H&E slide into three distinct regions associated with long-term survival, short-term survival, and non-informative tissue. We characterized these regions by integrating unsupervised learning, nuclei segmentation, blood vessels detection, pathologist annotations, and multimodal data including spatial transcriptomics from n=31 patients of the GBM MOSAIC dataset to discover fully interpretable biomarkers. ResultsOur OS prediction model using histology and clinical data as input achieved an area under the curve (AUC) of 0.85 for the classification of patients between OS<2 and OS[≥]3y in external cohort validation, outperforming significantly models trained on clinical data or on histology alone (AUC of 0.76; 0.73, respectively). Two novel biomarkers were predicting poor survival: the presence of regions of lowly infiltrated white matter enriched in malignant cells with a mesenchymal-like phenotype, and lower levels of angiogenesis associated with higher hypoxia response in the main tumor regions. We also found that a subtype of immunosuppressive tumor macrophages - defined by high PLIN2 expression and lipid accumulation- is consistently enriched in histological areas predictive of poor prognosis. ConclusionOur interpretable ML methodology identified a novel prognostic impact of biological processes and cell types according to distinct tumor regions of GBM. These results pave the way for spatially-informed biomarkers to improve risk stratification and for personalized spatially-targeted therapeutic strategies. Key highlightsO_LIOur ML model identified histological biomarkers predicting prognosis independently from known clinical factors C_LIO_LIThe region of lowly infiltrated white matter enriched in malignant cells including a mesenchymal-like phenotype is predictive of poor prognosis C_LIO_LIAngiogenesis is increased in areas predictive of long survival in main non-necrotic tumor regions. C_LIO_LIThe subtype of macrophages expressing PLIN2 and associated with increased lipid metabolism was associated with poor prognosis in all GBM regions. C_LI Highlights O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/681087v4_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@45e619org.highwire.dtl.DTLVardef@105a4b0org.highwire.dtl.DTLVardef@17f257dorg.highwire.dtl.DTLVardef@7669b4_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Tafazzin-Mediated Cardiolipin Remodeling Controls Metabolic Stress Response and Effector Function of Inflammatory T Cells

Clonal expansion and effector differentiation of T cells require extensive metabolic reprogramming. This includes the restructuring of the inner mitochondrial membrane (IMM) to enhance respiration by increasing chemiosmotic coupling efficiency. Cardiolipin, a unique phospholipid that is exclusively synthesized and localized in the IMM, modulates the biophysical properties of the electron transport chain (ETC) in tissues with high energy demands, such as cardiomyocytes and skeletal muscle. However, it remains unclear whether cardiolipin is also important for metabolic remodeling during T helper (Th) cell differentiation. In this study, we show that cardiolipin transacylation, catalyzed by the enzyme Tafazzin, supports the clonal expansion and effector function of inflammatory Th1 and Th17 cells in vitro and in models of autoimmune colitis and encephalomyelitis. At the molecular level, we demonstrate that loss of Tafazzin-mediated cardiolipin maturation induces a metabolic and transcriptional stress response in Th cells to compensate for impaired coupling efficiency of the ETC complexes and disrupted cellular redox homeostasis. However, the genetic program that restores cellular homeostasis and mitigates oxidative stress concurrently impairs the effector functions of inflammatory T cells, such as cytokine production. Our findings also provide insights into the complex clinical manifestation of patients with Barth syndrome (BTHS) caused by mutations in the human TAFAZZIN gene. BTHS is characterized by cardiac and skeletal myopathy as well as neutropenia and an increased susceptibility to infections. Although the molecular basis of the immunodeficiency remains poorly understood, our findings suggest that impaired Th cell function contributes to the immunopathology observed in BTHS patients.

immunology↗

bioRxiv2025

Spatial Transcriptomics Expression Prediction from Histopathology Based on Cross-Modal Mask Reconstruction and Contrastive Learning

Spatial transcriptomics is a technology that captures gene expression levels at different spatial locations, widely used in tumor microenvironment analysis and molecular profiling of histopathology, providing valuable insights into resolving gene expression and clinical diagnosis of cancer. Due to the high cost of data acquisition, large-scale spatial transcriptomics data remain challenging to obtain. In this study, we develop a contrastive learning-based deep learning method to predict spatially resolved gene expression from whole-slide images. Evaluation across six different disease datasets demonstrates that, compared to existing studies, our method improves Pearson Correlation Coefficient (PCC) in the prediction of highly expressed genes, highly variable genes, and marker genes by 6.27%, 6.11%, and 11.26% respectively. Further analysis indicates that our method preserves gene-gene correlations and applies to datasets with limited samples. Additionally, our method exhibits potential in cancer tissue localization based on biomarker expression.

pathology↗

bioRxiv2024

Emotion processing in mothers with bonding disorder during the first year postpartum: Results of an adapted fMRI emotional GoNoGo Task

Maternal bonding refers to the unique emotional connection between a mother and her baby that gradually develops during the peripartum period. However, 3-24% of women report bonding problems (BP), often accompanied by constraints for the mother-infant relationship, but not always depression with consequences for child development. Our present study investigates the neural and behavioral patterns that underlie the processing of emotional infant stimuli over the 1 year postpartum parallel to a neurofeedback intervention. Mothers with and without BP (N = 45) completed a newly developed Emotional Infant GoNoGo Task while fMRI scanning at 3, 6 and 12 months postpartum. Our results show that response inhibition towards emotional infant faces elicits stronger results than towards adult faces in all mothers. While behavioral performance in BP is impaired, the neural responses to emotional infant faces as compared to neutral faces are increased at 3 months postpartum in limbic structures such as the anterior cingulate and insula, as well as nucleus caudatus. At 6 and 12 months behavioral reactions assimilate in BP to those of healthy controls, while differences in neural reactions between BP and healthy controls increase at 6 months and decrease again at 12 months. These effects are independent of depressive symptoms. Our findings point to an experience-based adaptive process of emotion processing and responses to infants affect during the first year postpartum as a specific characteristic of clinical BP. Implications are for potential therapeutic interventions to target emotion processing and regulation. Key FindingsO_LIWe present an adapted version of an Emotional GoNoGo Task for fMRI with infant stimuli: Behavioral inhibition towards emotional infant faces leads to increased activity in emotion regulation networks compared to adult faces in women during the 1st year postpartum. C_LIO_LIWomen with postpartum bonding problems (BP) show increased neural reactions (ACC, NCl Caudate, Insula) towards emotional baby faces (not adult faces, irrespectively of instructed inhibition and of valence) compared to a healthy control group (CG) at 3 months postpartum. C_LIO_LIOn a behavioural level, women with BP show increased reaction times to positive emotional adult faces and higher error rates during the inhibition of reaction towards emotional baby faces at 3 months. C_LIO_LIDifferences in neural reactions to emotional baby faces between BP and CG increase at 6 months and decrease again at 12 months following a neurofeedback intervention, while behavioral reactions in BD assimilate to those of CG C_LI

neuroscience↗

bioRxiv2024

Aberrant expression of histone H2B variants reshape chromatin and alter oncogenic gene expression programs

Chromatin architecture governs DNA accessibility and gene expression. Thus, any perturbations to chromatin can significantly alter gene expression programs and promote disease. Prior studies demonstrate that every amino acid in a histone is functionally significant, and that even a single amino acid substitution can drive specific cancers. We previously observed that naturally occurring H2B variants are dysregulated during the epithelial to mesenchymal transition (EMT) in bronchial epithelial cells. Naturally occurring H2B variants differ from canonical H2B by only a few amino acids, yet single amino acid changes in other histone variants (e.g., H3.3) can drive cancer. We therefore hypothesized that H2B variants might function like oncohistones, and investigated how they modify chromatin architecture, dynamics, and function. We find that H2B variants are frequently dysregulated in many cancers, and correlate with patient prognosis. Despite high sequence similarity, mutations in each H2B variant tend to occur at specific "hotspots" in cancer. Some H2B variants cause tighter DNA wrapping around nucleosomes, leading to more compact chromatin structures and reduced transcription factor accessibility to nucleosomal DNA. They also altered genome-wide accessibility to oncogenic regulatory elements and genes, with concomitant changes in oncogenic gene expression programs. Although we did not observe changes in cell proliferation or migration in vitro, our Gene Ontology (GO) analyses of ATAC-seq peaks and RNA-seq data indicated significant changes in oncogenic pathways. These findings suggest that H2B variants may influence early-stage, cancer-associated regulatory mechanisms, potentially setting the stage for oncogenesis later on. Thus, H2B variant expression could serve as an early cancer biomarker, and H2B variants might be novel therapeutic targets.

cancer biology↗

bioRxiv2024

Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients.

Over the last decade, Hippo signaling has emerged as a major tumor-suppressing pathway. Its dysregulation is associated with abnormal expression of YAP1 and TEAD-family genes. Recent works have highlighted the role of YAP1/TEAD activity in several cancers and its potential therapeutic implications. Therefore, identifying patients with a dysregulated Hippo pathway is key to enhancing treatment impact. Although recent studies have derived RNAseq-based signatures, there remains a need for a reproducible and cost-effective method to measure the pathway activation. In recent years, deep learning applied to histology slides have emerged as an effective way to predict molecular information from a data modality available in clinical routine. Here, we trained models to predict YAP1/TEAD activity from H&E-stained histology slides in multiple cancers. The robustness of our approach was assessed in seven independent validation cohorts. Finally, we showed that histological markers of disease aggressiveness were associated with dysfunctional Hippo signaling.

bioinformatics↗

bioRxiv2023

HIF-1α-mediated mitochondrial-glycolytic reprogramming controls the transition of precursor to terminally exhausted T cells

Functional exhaustion of T cells in cancer and persistent infections is characterized by the upregulation of inhibitory receptors, the progressive decline in cytokine secretion and impaired cytolytic activity. Terminally exhausted T cells are steadily replenished by a precursor population (Tpex) with phenotypic features of memory T cells and a stem-like capacity to self-renew. However, the metabolic principles of Tpex maintenance and the regulatory circuits that control the exhaustion of their progeny remain incompletely understood. Using a combination of gene-deficient mice, single-cell transcriptomics and metabolomic analyses, we here show that mitochondrial insufficiency is a cell-intrinsic trigger that initiates the T cell exhaustion program. At the molecular level, we found that diminished mitochondrial respiration and metabolic remodeling cause oxidative stress, which inhibits the proteasomal degradation of hypoxia inducible factor 1 alpha (HIF-1) in Tpex cells. HIF-1 mediates the transcriptional-glycolytic reprogramming of Tpex cells as an initial step towards terminal differentiation and functional exhaustion. Finally, we show that enhancing respiration by limiting the glycolytic activity of CAR T cells is a feasible metabolic intervention strategy to preserve the stemness of Tpex cells during chronic viral infection and cancer immunotherapy.

immunology↗

bioRxiv2023

Cognitive Model Discovery via Disentangled RNNs

Computational cognitive models are a fundamental tool in behavioral neuroscience. They instantiate in software precise hypotheses about the cognitive mechanisms underlying a particular behavior. Constructing these models is typically a difficult iterative process that requires both inspiration from the literature and the creativity of an individual researcher. Here, we adopt an alternative approach to learn parsimonious cognitive models directly from data. We fit behavior data using a recurrent neural network that is penalized for carrying information forward in time, leading to sparse, interpretable representations and dynamics. When fitting synthetic behavioral data from known cognitive models, our method recovers the underlying form of those models. When fit to laboratory data from rats performing a reward learning task, our method recovers simple and interpretable models that make testable predictions about neural mechanisms.

neuroscience↗