Dual Pharmacological Targeting of HDACs and PDE5 Inhibits Liver Disease Progression in a Mouse Model of Biliary Inflammation and Fibrosis

Liver fibrosis, a common hallmark of chronic liver disease (CLD), is characterized by the accumulation of extracellular matrix secreted by activated hepatic fibroblasts and stellate cells (HSC). Fibrogenesis involves multiple cellular and molecular processes and is intimately linked to chronic hepatic inflammation. Importantly, it has been shown to promote the loss of liver function and liver carcinogenesis. No effective therapies for liver fibrosis are currently available. We examined the anti-fibrogenic potential of a new drug (CM414) that simultaneously inhibits histone deacetylases (HDACs), more precisely HDAC1, 2, and 3 (Class I) and HDAC6 (Class II) and stimulates the cyclic guanosine monophosphate (cGMP)-protein kinase G (PKG) pathway activity through phosphodiesterase 5 (PDE5) inhibition, two mechanisms independently involved in liver fibrosis. To this end, we treated Mdr2-KO mice, a clinically relevant model of liver inflammation and fibrosis, with our dual HDAC/PDE5 inhibitor CM414. We observed a decrease in the expression of fibrogenic markers and collagen deposition, together with a marked reduction in inflammation. No signs of hepatic or systemic toxicity were recorded. Mechanistic studies in cultured human HSC and cholangiocytes (LX2 and H69 cell lines, respectively) demonstrated that CM414 inhibited pro-fibrogenic and inflammatory responses, including those triggered by transforming growth factor β (TGFβ). Our study supports the notion that simultaneous targeting of pro-inflammatory and fibrogenic mechanisms controlled by HDACs and PDE5 with a single molecule, such as CM414, can be a new disease-modifying strategy

Dual Pharmacological Targeting of HDACs and PDE5 Inhibits Liver Disease Progression in a Mouse Model of Biliary Inflammation and Fibrosis

Liver fibrosis, a common hallmark of chronic liver disease (CLD), is characterized by the accumulation of extracellular matrix secreted by activated hepatic fibroblasts and stellate cells (HSC). Fibrogenesis involves multiple cellular and molecular processes and is intimately linked to chronic hepatic inflammation. Importantly, it has been shown to promote the loss of liver function and liver carcinogenesis. No effective therapies for liver fibrosis are currently available. We examined the anti-fibrogenic potential of a new drug (CM414) that simultaneously inhibits histone deacetylases (HDACs), more precisely HDAC1, 2, and 3 (Class I) and HDAC6 (Class II) and stimulates the cyclic guanosine monophosphate (cGMP)-protein kinase G (PKG) pathway activity through phosphodiesterase 5 (PDE5) inhibition, two mechanisms independently involved in liver fibrosis. To this end, we treated Mdr2-KO mice, a clinically relevant model of liver inflammation and fibrosis, with our dual HDAC/PDE5 inhibitor CM414. We observed a decrease in the expression of fibrogenic markers and collagen deposition, together with a marked reduction in inflammation. No signs of hepatic or systemic toxicity were recorded. Mechanistic studies in cultured human HSC and cholangiocytes (LX2 and H69 cell lines, respectively) demonstrated that CM414 inhibited pro-fibrogenic and inflammatory responses, including those triggered by transforming growth factor β (TGFβ). Our study supports the notion that simultaneous targeting of pro-inflammatory and fibrogenic mechanisms controlled by HDACs and PDE5 with a single molecule, such as CM414, can be a new disease-modifying strategy

Identification and experimental validation of druggable epigenetic targets in hepatoblastoma

Background & Aims: Hepatoblastoma (HB) is the most frequent childhood liver cancer. Patients with aggressive tumors have limited therapeutic options; therefore, a better understanding of HB pathogenesis is needed to improve treatment. HBs have a very low mutational burden; however, epigenetic alterations are increasingly recognized. We aimed to identify epigenetic regulators consistently dysregulated in HB and to evaluate the therapeutic efficacy of their targeting in clinically relevant models. Methods: We performed a comprehensive transcriptomic analysis of 180 epigenetic genes. Data from fetal, pediatric, adult, peritumoral (n = 72) and tumoral (n = 91) tissues were integrated. Selected epigenetic drugs were tested in HB cells. The most relevant epigenetic target identified was validated in primary HB cells, HB organoids, a patient-derived xenograft model, and a genetic mouse model. Transcriptomic, proteomic and metabolomic mechanistic analyses were performed. Results: Altered expression of genes regulating DNA methylation and histone modifications was consistently observed in association with molecular and clinical features of poor prognosis. The histone methyltransferase G9a was markedly upregulated in tumors with epigenetic and transcriptomic traits of increased malignancy. Pharmacological targeting of G9a significantly inhibited growth of HB cells, organoids and patient-derived xenografts. Development of HB induced by oncogenic forms of b-catenin and YAP1 was ablated in mice with hepatocyte-specific deletion of G9a. We observed that HBs undergo significant transcriptional rewiring in genes involved in amino acid metabolism and ribosomal biogenesis. G9a inhibition counteracted these pro-tumorigenic adaptations. Mechanistically, G9a targeting potently repressed the expression of c-MYC and ATF4, master regulators of HB metabolic reprogramming. Conclusions: HBs display a profound dysregulation of the epigenetic machinery. Pharmacological targeting of key epigenetic effectors exposes metabolic vulnerabilities that can be leveraged to improve the treatment of these patients

Pilot multi-omic analysis of human bile from benign and malignant biliary strictures: A machine-learning approach

Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused by benign conditions, and the identification of their etiology still remains a clinical challenge. We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36) and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (1H-NMR) in all patients. MS analysis of bile proteome was performed in five patients per group. We implemented artificial intelligence tools for the selection of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included the generation of synthetic data with properties of real data, the selection of potential biomarkers (metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when analyzed with NN algorithms discriminated between patients with and without cancer with an unprecedented accurac

Pilot multi-omic analysis of human bile from benign and malignant biliary strictures: A machine-learning approach

Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused by benign conditions, and the identification of their etiology still remains a clinical challenge. We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36) and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (1H-NMR) in all patients. MS analysis of bile proteome was performed in five patients per group. We implemented artificial intelligence tools for the selection of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included the generation of synthetic data with properties of real data, the selection of potential biomarkers (metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when analyzed with NN algorithms discriminated between patients with and without cancer with an unprecedented accurac