Dynamic YAP expression in the non-parenchymal liver cell compartment controls heterologous cell communication

INTRODUCTION

The Hippo pathway and its transcriptional effectors yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ) are targets for cancer therapy. It is important to determine if the activation of one factor compensates for the inhibition of the other. Moreover, it is unknown if YAP/TAZ-directed perturbation affects cell-cell communication of non-malignant liver cells.

MATERIALS AND METHODS

To investigate liver-specific phenotypes caused by YAP and TAZ inactivation, we generated mice with hepatocyte (HC) and biliary epithelial cell (BEC)-specific deletions for both factors (YAPKO, TAZKO and double knock-out (DKO)). Immunohistochemistry, single-cell sequencing, and proteomics were used to analyze liver tissues and serum.

RESULTS

The loss of BECs, liver fibrosis, and necrosis characterized livers from YAPKO and DKO mice. This phenotype was weakened in DKO tissues compared to specimens from YAPKO animals. After depletion of YAP in HCs and BECs, YAP expression was induced in non-parenchymal cells (NPCs) in a cholestasis-independent manner. YAP positivity was detected in subgroups of Kupffer cells (KCs) and endothelial cells (ECs). The secretion of pro-inflammatory chemokines and cytokines such as C-X-C motif chemokine ligand 11 (CXCL11), fms-related receptor tyrosine kinase 3 ligand (FLT3L), and soluble intercellular adhesion molecule-1 (ICAM1) was increased in the serum of YAPKO animals. YAP activation in NPCs could contribute to inflammation via TEA domain transcription factor (TEAD)-dependent transcriptional regulation of secreted factors.

CONCLUSION

YAP inactivation in HCs and BECs causes liver damage, and concomitant TAZ deletion does not enhance but reduces this phenotype. Additionally, we present a new mechanism by which YAP contributes to cell-cell communication originating from NPCs.

signifinder enables the identification of tumor cell states and cancer expression signatures in bulk, single-cell and spatial transcriptomic data

Over the last decade, many studies and some clinical trials have proposed gene expression signatures as a valuable tool for understanding cancer mechanisms, defining subtypes, monitoring patient prognosis, and therapy efficacy. However, technical and biological concerns about reproducibility have been raised. Technical reproducibility is a major concern: we currently lack a computational implementation of the proposed signatures, which would provide detailed signature definition and assure reproducibility, dissemination, and usability of the classifier. Another concern regards intratumor heterogeneity, which has never been addressed when studying these types of biomarkers using bulk transcriptomics. With the aim of providing a tool able to improve the reproducibility and usability of gene expression signatures, we propose signifinder, an R package that provides the infrastructure to collect, implement, and compare expression-based signatures from cancer literature. The included signatures cover a wide range of biological processes from metabolism and programmed cell death, to morphological changes, such as quantification of epithelial or mesenchymal-like status. Collected signatures can score tumor cell characteristics, such as the predicted response to therapy or the survival association, and can quantify microenvironmental information, including hypoxia and immune response activity. signifinder has been used to characterize tumor samples and to investigate intra-tumor heterogeneity, extending its application to single-cell and spatial transcriptomic data. Through these higher-resolution technologies, it has become increasingly apparent that the single-sample score assessment obtained by transcriptional signatures is conditioned by the phenotypic and genetic intratumor heterogeneity of tumor masses. Since the characteristics of the most abundant cell type or clone might not necessarily predict the properties of mixed populations, signature prediction efficacy is lowered, thus impeding effective clinical diagnostics. Through signifinder, we offer general principles for interpreting and comparing transcriptional signatures, as well as suggestions for additional signatures that would allow for more complete and robust data inferences. We consider signifinder a useful tool to pave the way for reproducibility and comparison of transcriptional signatures in oncology.