In the mammalian liver, hepatocytes exhibit diverse metabolic and functional profiles based on their location within the liver lobule. However, it is unclear whether this spatial variation, called zonation, is governed by a well-defined gene regulatory code. Here, using a combination of single-cell multiomics, spatial omics, massively parallel reporter assays and deep learning, we mapped enhancer-gene regulatory networks across mouse liver cell types. We found that zonation affects gene expression and chromatin accessibility in hepatocytes, among other cell types. These states are driven by the repressors TCF7L1 and TBX3, alongside other core hepatocyte transcription factors, such as HNF4A, CEBPA, FOXA1 and ONECUT1. To examine the architecture of the enhancers driving these cell states, we trained a hierarchical deep learning model called DeepLiver. Our study provides a multimodal understanding of the regulatory code underlying hepatocyte identity and their zonation state that can be used to engineer enhancers with specific activity levels and zonation patterns.

The Hippo signaling pathway is widely considered a master regulator of organ growth because of the prominent overgrowth phenotypes caused by experimental manipulation of its activity. Contrary to this model, we show here that removing Hippo transcriptional output did not impair the ability of the mouse liver and Drosophila eyes to grow to their normal size. Moreover, the transcriptional activity of the Hippo pathway effectors Yap/Taz/Yki did not correlate with cell proliferation, and hyperactivation of these effectors induced gene expression programs that did not recapitulate normal development. Concordantly, a functional screen in Drosophila identified several Hippo pathway target genes that were required for ectopic overgrowth but not normal growth. Thus, Hippo signaling does not instruct normal growth, and the Hippo-induced overgrowth phenotypes are caused by the activation of abnormal genetic programs.

The Hippo pathway and its downstream effectors, the YAP and TAZ transcriptional coactivators, are deregulated in multiple different types of human cancer and are required for cancer cell phenotypes in vitro and in vivo, while largely dispensable for tissue homeostasis in adult mice. YAP/TAZ and their main partner transcription factors, the TEAD1–4 factors, are therefore promising anticancer targets. Because of frequent YAP/TAZ hyperactivation caused by mutations in the Hippo pathway components NF2 and LATS2, mesothelioma is one of the prime cancer types predicted to be responsive to YAP/TAZ-TEAD inhibitor treatment. Mesothelioma is a devastating disease for which currently no effective treatment options exist. Here, we describe a novel covalent YAP/TAZ-TEAD inhibitor, SWTX-143, that binds to the palmitoylation pocket of all four TEAD isoforms. SWTX-143 caused irreversible and specific inhibition of the transcriptional activity of YAP/TAZ-TEAD in Hippo-mutant tumor cell lines. More importantly, YAP/TAZ-TEAD inhibitor treatment caused strong mesothelioma regression in subcutaneous xenograft models with human cells and in an orthotopic mesothelioma mouse model. Finally, SWTX-143 also selectively impaired the growth of NF2-mutant kidney cancer cell lines, suggesting that the sensitivity of mesothelioma models to these YAP/TAZ-TEAD inhibitors can be extended to other tumor types with aberrations in Hippo signaling. In brief, we describe a novel and specific YAP/TAZ-TEAD inhibitor that has potential to treat multiple Hippo-mutant solid tumor types.

Metabolic dysfunction-associated steatotic liver disease (MASLD) and its advanced form, metabolic dysfunction-associated steatohepatitis (MASH), are leading risk factors for liver cancer. While inflammation and fibrosis are known to promote tumorigenesis in MASH, the role of early MASLD in cancer initiation is unclear. Here, we investigated how steatosis alone, without significant fibrosis and inflammation, influences liver cancer development in mouse models. We fed mice a Western diet to induce early-stage MASLD, characterized by steatosis and mild hepatitis without fibrosis, and triggered liver tumorigenesis by hydrodynamic tail vein injection of plasmids encoding oncogenes. We combined this with genetic manipulation of Hippo signaling and assessed tumor development using histology and single-nucleus RNA sequencing. Western diet suppressed liver tumor development by promoting the elimination of tumor-initiating cells. This effect was not due to a direct impact on oncogene-expressing cells, but rather to increased competitiveness of the surrounding hepatocytes. Mechanistically, the Western diet activated the Hippo pathway effectors Yap and Taz in steatotic hepatocytes, enabling them to outcompete tumor-initiating cells. Progression to advanced MASH abrogated this tumor-suppressing effect, allowing tumor development. Steatotic hepatocytes in early MASLD activate a Yap/Taz-dependent cell competition program that can suppress tumor initiation. This tumor-suppressive effect is lost in advanced MASH, where inflammation, fibrosis and other defects undermine the competitiveness of non-mutant hepatocytes. The tumor-suppressive effect of early MASLD adds a new layer to our understanding of how fitness landscapes and cell interactions shape cancer risk during chronic liver disease. We found that early stages of steatotic liver disease (MASLD) can suppress tumor initiation in mouse models of liver cancer by triggering Yap/Taz-mediated cell competition. This reveals an unexpected tumor-suppressive role for early MASLD and highlights the importance of non-cell autonomous mechanisms during tumor initiation. Since Hippo-YAP/TAZ signaling is deregulated in many cancers, similar mechanisms may operate in other tissues.

Tracking cell death in vivo can enable a better understanding of the biological mechanisms underlying tissue homeostasis and disease. Unfortunately, existing cell death labeling methods lack compatibility with in vivo applications or suffer from low sensitivity, poor tissue penetration, and limited temporal resolution. Here, we fluorescently labeled dead cells in vivo with Trypan Blue (TBlue) to detect single scattered dead cells or to generate whole-mount three-dimensional maps of large areas of necrotic tissue during organ regeneration. TBlue effectively marked different types of cell death, including necrosis induced by CCl₄ intoxication in the liver, necrosis caused by ischemia-reperfusion in the skin, and apoptosis triggered by BAX overexpression in hepatocytes. Moreover, due to its short circulating lifespan in blood, TBlue labeling allowed in vivo “pulse and chase” tracking of two temporally spaced populations of dying hepatocytes in regenerating mouse livers. Additionally, upon treatment with cisplatin, TBlue labeled dead cancer cells in livers with cholangiocarcinoma and dead thymocytes due to chemotherapy-induced toxicity, showcasing its utility in assessing anticancer therapies in preclinical models. Thus, TBlue is a sensitive and selective cell death marker for in vivo applications, facilitating the understanding of the fundamental role of cell death in normal biological processes and its implications in disease.

Cell type identity is encoded by gene regulatory networks (GRN), in which transcription factors (TFs) bind to enhancers to regulate target gene expression. In the mammalian liver, lineage TFs have been characterized for the main cell types, including hepatocytes. Hepatocytes cover a relatively broad cellular state space, as they differ significantly in their metabolic state, and function, depending on their position with respect to the central or portal vein in a liver lobule. It is unclear whether this spatially defined cellular state space, called zonation, is also governed by a well-defined gene regulatory code. To address this challenge, we have mapped enhancer-GRNs across liver cell types at high resolution, using a combination of single cell multiomics, spatial omics, GRN inference, and deep learning. We found that cell state changes in transcription and chromatin accessibility in hepatocytes, liver sinusoidal endothelial cells and hepatic stellate cells depend on zonation. Enhancer-GRN mapping suggests that zonation states in hepatocytes are driven by the repressors Tcf7l1 and Tbx3, that modulate the core hepatocyte GRN, controlled by Hnf4a, Cebpa, Hnf1a, Onecut1 and Foxa1, among others. To investigate how these TFs cooperate with cell type TFs, we performed an in vivo massively parallel reporter assay on 12,000 hepatocyte enhancers and used these data to train a hierarchical deep learning model (called DeepLiver) that exploits both enhancer accessibility and activity. DeepLiver confirms Cebpa, Onecut, Foxa1, Hnf1a and Hnf4a as drivers of enhancer specificity in hepatocytes; Tcf7l1/2 and Tbx3 as regulators of the zonation state; and Hnf4a, Hnf1a, AP-1 and Ets as activators. Finally, taking advantage of in silico mutagenesis predictions from DeepLiver and enhancer assays, we confirmed that the destruction of Tcf7l1/2 or Tbx3 motifs in zonated enhancers abrogates their zonation bias. Our study provides a multi-modal understanding of the regulatory code underlying hepatocyte identity and their zonation state, that can be exploited to engineer enhancers with specific activity levels and zonation patterns.