KRAS-dependent acinar-to-ductal metaplasia (ADM) is a fundamental step in the development of pancreatic ductal adenocarcinoma (PDAC), but the involvement of cell death pathways remains unclear. Here, we show that key regulators of programmed cell death (PCD) become upregulated during KRAS-driven ADM, thereby priming transdifferentiated cells to death. Using transgenic mice and primary cell and organoid cultures, we show that transforming growth factor (TGF)-β-activated kinase 1 (TAK1), a kinase regulating cell survival and inflammatory pathways, prevents the elimination of transdifferentiated cells through receptor-interacting protein kinase 1 (RIPK1)-mediated apoptosis and necroptosis, enabling PDAC development. Accordingly, pharmacological inhibition of TAK1 induces PCD in patient-derived PDAC organoids. Importantly, cell death induction via TAK1 inhibition does not appear to elicit an overt injury-associated inflammatory response. Collectively, these findings suggest that TAK1 supports cellular plasticity by suppressing spontaneous PCD activation during ADM, representing a promising pharmacological target for the prevention and treatment of PDAC.

Objective Impaired hepatic expression of protein tyrosine phosphatase delta (PTPRD) is associated with increased STAT3 transcriptional activity and reduced survival from hepatocellular carcinoma in patients with chronic hepatitis C virus infection. However, the PTPRD-expressing hepatic cell types, signalling pathways responsive to PTPRD and their role in non-viral liver disease are largely unknown.Methods We studied PTPRD expression in single-cell and bulk liver transcriptomic data from mice and humans, and established a Ptprd-deficient mouse model for metabolic dysfunction-associated steatohepatitis (MASH). Identified pathways were validated by perturbation studies in human hepatocytes and PTPRD substrates by pull-down assays. The clinical relevance was further explored in a cohort with metabolic disease by ranking patients according to PTPRD expression and analysing its association with metabolic disease markers.Results The analysis of individuals ranked according to PTPRD expression and Ptprd-deficient mice, showed that PTPRD levels were associated with hepatic glucose/lipid signalling and peroxisome function. Hepatic PTPRD expression is impaired in aetiologies of chronic liver diseases that are associated with metabolic disease. We further validated PTPRD as a STAT3 phosphatase in the liver, acting as a regulator of peroxisomal fatty acid metabolism. During MASH, low PTPRD led to increased liver steatosis in Ptprd+/− mice and a pronounced unfolded protein response, which impacts insulin signalling. Accordingly, silencing of PTPRD blunted insulin-induced AKT phosphorylation. Patients with obesity and low hepatic PTPRD expression exhibit increased levels of metabolic risk factors.Conclusion Our data revealed an important regulatory role of the hepatic PTPRD-STAT3 axis in maintaining glucose/lipid homeostasis, which is recapitulated in clinical manifestations of metabolic liver disease.

BACKGROUND & AIMS:Liver fibrosis is the major driver of hepatocellular carcinoma and liver disease-related death. Approved antifibrotic therapies are absent and compounds in development have limited efficacy. Increased TGF-β signaling drives collagen deposition by hepatic stellate cells (HSCs)/myofibroblasts. Here, we aimed to dissect the role of the circadian clock (CC) in controlling TGF-β signaling and liver fibrosis. METHODS:Using CC-mutant mice, enriched HSCs and myofibroblasts obtained from healthy and fibrotic mice in different CC phases and loss-of-function studies in human hepatocytes and myofibroblasts, we investigated the relationship between CC and TGF-β signaling. We explored hepatocyte-myofibroblast communication through bioinformatic analyses of single-nuclei transcriptomes and performed validation in cell-based models. Using mouse models for MASH (metabolic dysfunction-associated steatohepatitis)-related fibrosis and spheroids from patients with liver disease, we performed proof-of-concept studies to validate pharmacological targetability and clinical translatability. RESULTS:We discovered that the CC oscillator temporally gates TGF-β signaling and this regulation is broken in fibrosis. We demonstrate that HSCs and myofibroblasts contain a functional CC with rhythmic expression of numerous genes, including fibrogenic genes. Perturbation studies in hepatocytes and myofibroblasts revealed a reciprocal relationship between TGF-β activation and CC perturbation, which was confirmed in patient-derived ex vivo and in vivo models. Pharmacological modulation of CC-TGF-β signaling inhibited fibrosis in mouse models in vivo as well as in patient-derived liver spheroids. CONCLUSION:The CC regulates TGF-β signaling, and the breakdown of this control is associated with liver fibrosis in patients. Pharmacological proof-of-concept studies across different models have uncovered the CC as a novel therapeutic target for liver fibrosis - a growing unmet medical need. IMPACT AND IMPLICATIONS:Liver fibrosis due to metabolic diseases is a global health challenge. Many liver functions are rhythmic throughout the day, being controlled by the circadian clock (CC). Here we demonstrate that regulation of the CC is perturbed upon chronic liver injury and this perturbation contributes to fibrotic disease. By showing that a compound targeting the CC improves liver fibrosis in patient-derived models, this study provides a novel therapeutic candidate strategy to treat fibrosis in patients. Additional studies will be needed for clinical translation. Since the findings uncover a previously undiscovered profibrotic mechanism and therapeutic target, the study is of interest for scientists investigating liver disease, clinical hepatologists and drug developers.

Abstract Introduction: Cholangiocarcinoma (CCA) is a highly aggressive adenocarcinoma of the hepatobiliary system showing an alarming rise in incidence and mortality with unsatisfactory treatment options. Claudin-1 (CLDN1) is a transmembrane protein expressed in tight junctions, but exposed at the cell surface on cancer epithelial cells. Using highly specific monoclonal antibodies (mAbs) targeting the extracellular loop 1 of exposed CLDN1 with an excellent safety profile (Roehlen, Saviano et al. Science Transl Med 2022), we aimed to investigate the role of CLDN1 as therapeutic target for CCA. Methods: Integrative CCA patient CLDN1 expression analyses, spatial transcriptomics and mouse models were used to evaluate the role of CLDN1 as an oncogenic driver for CCA. Proof-of-concept studies were performed in cell line-derived xenograft (CDX) and patient-derived xenograft (PDX) mouse as well as patient CCA organoid models using humanized CLDN1 mAbs. Results: Integrative expression analyses of CLDN1 in CCA patient tissues revealed robust CLDN1 upregulation across several cohorts and patients with well-characterized driver mutations. scRNASeq and spatial transcriptomics of patient CCA showed that CLDN1 expression in cancer cells is associated with stemness, oncogenic signaling and EMT. Gain-of-function studies using an orthotopic HDTVi and syngeneic mouse model revealed a decrease in survival and an enhanced tumor growth, unraveling a functional role of CLDN1 as an oncogenic driver in CCA. Targeting exposed CLDN1 using highly CLDN1-specific mAb demonstrated a robust anti-tumoral effect across intra- and extra-hepatic CCA mouse models, with a significant inhibition of metastatic disease including models with medium or low CLDN1 expression. Functional studies in patient-derived CCA organoids demonstrated that CLDN1 mAb decreased cellular viability and altered cancer cell plasticity and fate. Mechanistically, CLDN1 mAb treatment suppressed gene expression of pathways mediating proliferation, stemness and EMT by inhibition of Notch1, SRC-FAK, and Hippo-YAP signaling. Conclusion: These results demonstrate that CLDN1 is a CCA driver and therapeutic target. The proof-of-concept studies in patient-derived models pave the way for the clinical development of CLDN1 mAbs to improve the outcomes of patients with advanced CCA. Citation Format: Zeina Nehme, Marion Muller, Emilie Crouchet, Frank Juehling, Julien Moehlin, Romain Désert, Jade Brochon, Fabio Del Zompo, Natascha Roehlen, Christine Thumann, Patrick Pessaux, Emanuele Felli, Aïna Venkatasamy, Patrice Marchand, Mihaela Alina Onea, Roberto Iacone, Markus Meyer, Alberto Toso, Nabeel Bardeesy, Lipika Goyal, Vikas Prakash Ranvir, Mirian Fernández-Vaquero, Mathias Heikenwälder, Tessa Ostyn, Tania Roskams, Patrice Laquerriere, Catherine Schuster, Laurent Mailly, Thomas F. Baumert. Treatment of cholangiocarcinoma using humanized monoclonal antibodies targeting claudin-1 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5981.

Spatially resolved transcriptomics is revolutionizing our understanding of complex tissues, but scaling these ap-proaches to multiple tissue sections and three-dimensional tissue reconstruction remains challenging and cost prohibitive. In this work, we present a low-cost strategy for manufacturing molecularly double-barcoded DNA arrays, enabling large-scale spatially resolved transcriptomics studies. We applied this technique to spatially resolve gene expression in several human brain organoids, including the reconstruction of a three-dimensional view from multiple consecutive sections, revealing gene expression heterogeneity throughout the tissue.

Spatially resolved transcriptomics (SrT) allow researchers to explore organ/tissue architecture from the angle of the gene programs involved in their molecular complexity. Here, we describe the use of MULTILAYER to reveal molecular tissue substructures from the analysis of localized transcriptomes (defined as gexels). MULTILAYER can process low- and high-resolution SrT data but also perform comparative analyses within multiple SrT readouts. For complete details on the use and execution of this protocol, please refer to Moehlin et al., 2021.

Spatially resolved transcriptomics (SrT) can investigate organ or tissue architecture from the angle of gene programs that define their molecular complexity. However, computational methods to analyze SrT data underexploit their spatial signature. Inspired by contextual pixel classification strategies applied to image analysis, we developed MULTILAYER to stratify maps into functionally relevant molecular substructures. MULTILAYER applies agglomerative clustering within contiguous locally defined transcriptomes (gene expression elements or "gexels") combined with community detection methods for graphical partitioning. MULTILAYER resolves molecular tissue substructures within a variety of SrT data with superior performance to commonly used dimensionality reduction strategies and still detects differentially expressed genes on par with existing methods. MULTILAYER can process high-resolution as well as multiple SrT data in a comparative mode, anticipating future needs in the field. MULTILAYER provides a digital image perspective for SrT analysis and opens the door to contextual gexel classification strategies for developing self-supervised molecular diagnosis solutions. A record of this paper's transparent peer review process is included in the supplemental information.

SummaryDevelopments on spatial transcriptomics (ST) are providing means to interrogate organ/tissue architecture from the angle of the gene programs defining their molecular complexity. However, computational methods to analyze ST data under-exploits the spatial signature retrieved within the maps. Inspired by contextual pixel classification strategies applied to image analysis, we have developed MULTILAYER, allowing to stratify ST maps into functionally-relevant molecular substructures. For it, MULTILAYER applies agglomerative clustering strategies within contiguous locally-defined transcriptomes (herein defined as gene expression elements or Gexels), combined with community detection methods for graph partitioning.MULTILAYER has been evaluated over multiple public ST data, including developmental tissues but also tumor biopsies. Its performance has been challenged for the processing of high-resolution ST maps and it has been used for an enhanced comparison of multiple public tissue biopsies issued from a cancerous prostate.MULTILAYER provides a digital perspective for the analysis of spatially-resolved transcriptomes and anticipates the application of contextual gexel classification strategies for developing self-supervised molecular diagnostics solutions.Overall, the development of MULTILAYER anticipates the application of contextual gexel classification strategies for developing self-supervised molecular diagnostics solutions.

The enormous amount of freely accessible functional genomics data is an invaluable resource for interrogating the biological function of multiple DNA-interacting players and chromatin modifications by large-scale comparative analyses. However, in practice, interrogating large collections of public data requires major efforts for (i) reprocessing available raw reads, (ii) incorporating quality assessments to exclude artefactual and low-quality data, and (iii) processing data by using high-performance computation. Here, we present qcGenomics , a user-friendly online resource for ultrafast retrieval, visualization, and comparative analysis of tens of thousands of genomics datasets to gain new functional insight from global or focused multidimensional data integration.

Complex organisms originate from and are maintained by the information encoded in the genome. A major challenge of systems biology is to develop algorithms that describe the dynamic regulation of genome functions from large omics datasets. Here, we describe TETRAMER, which reconstructs gene-regulatory networks from temporal transcriptome data during cell fate transitions to predict "master" regulators by simulating cascades of temporal transcription-regulatory events.