Publications

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81 Publications visible to you, out of a total of 81

Abstract (Expand)

Lipid-based RNA nanocarriers have been recently accepted as a novel therapeutic option in humans, thus increasing the therapeutic options for patients. Tailored nanomedicines will enable to treat chronic liver disease (CLD) and end-stage liver cancer, disorders with high mortality and few treatment options. Here, we investigated the curative potential of gene therapy of a key molecule in CLD, the c-Jun N-terminal kinase-2 (Jnk2). Delivery to hepatocytes was achieved using a lipid-based clinically employable siRNA formulation that includes a cationic aminolipid to knockdown Jnk2 (named siJnk2). After assessing the therapeutic potential of siJnk2 treatment, non-invasive imaging demonstrated reduced apoptotic cell death and improved hepatocarcinogenesis was evidenced by improved liver parenchyma as well as ameliorated markers of hepatic damage, reduced fibrogenesis in 1-year-old mice. Strikingly, chronic siJnk2 treatment reduced premalignant nodules, indicative of tumor initiation. Furthermore, siJnk2 treatment led to a significant activation of the immune cell compartment. In conclusion, Jnk2 knockdown in hepatocytes ameliorated hepatitis, fibrogenesis, and initiation of hepatocellular carcinoma (HCC), and hence might be a suitable therapeutic option, to define novel molecular targets for precision medicine in CLD.

Authors: Marius Maximilian Woitok, Miguel Eugenio Zoubek, Dennis Doleschel, Matthias Bartneck, Mohamed Ramadan Mohamed, Fabian Kießling, Wiltrud Lederle, Christian Trautwein, Francisco Javier Cubero

Date Published: 1st May 2020

Publication Type: Journal

Abstract (Expand)

BACKGROUND: Although metabolism is profoundly altered in human liver cancer, the extent to which experimental models, e.g. cell lines, mimic those alterations is unresolved. Here, we aimed to determine the resemblance of hepatocellular carcinoma (HCC) cell lines to human liver tumours, specifically in the expression of deregulated metabolic targets in clinical tissue samples. METHODS: We compared the overall gene expression profile of poorly-differentiated (HLE, HLF, SNU-449) to well-differentiated (HUH7, HEPG2, HEP3B) HCC cell lines in three publicly available microarray datasets. Three thousand and eighty-five differentially expressed genes in >/=2 datasets (P < 0.05) were used for pathway enrichment and gene ontology (GO) analyses. Further, we compared the topmost gene expression, pathways, and GO from poorly differentiated cell lines to the pattern from four human HCC datasets (623 tumour tissues). In well- versus poorly differentiated cell lines, and in representative models HLE and HUH7 cells, we specifically assessed the expression pattern of 634 consistently deregulated metabolic genes in human HCC. These data were complemented by quantitative PCR, proteomics, metabolomics and assessment of response to thirteen metabolism-targeting compounds in HLE versus HUH7 cells. RESULTS: We found that poorly-differentiated HCC cells display upregulated MAPK/RAS/NFkB signaling, focal adhesion, and downregulated complement/coagulation cascade, PPAR-signaling, among pathway alterations seen in clinical tumour datasets. In HLE cells, 148 downregulated metabolic genes in liver tumours also showed low gene/protein expression - notably in fatty acid beta-oxidation (e.g. ACAA1/2, ACADSB, HADH), urea cycle (e.g. CPS1, ARG1, ASL), molecule transport (e.g. SLC2A2, SLC7A1, SLC25A15/20), and amino acid metabolism (e.g. PHGDH, PSAT1, GOT1, GLUD1). In contrast, HUH7 cells showed a higher expression of 98 metabolic targets upregulated in tumours (e.g. HK2, PKM, PSPH, GLUL, ASNS, and fatty acid synthesis enzymes ACLY, FASN). Metabolomics revealed that the genomic portrait of HLE cells co-exist with profound reliance on glutamine to fuel tricarboxylic acid cycle, whereas HUH7 cells use both glucose and glutamine. Targeting glutamine pathway selectively suppressed the proliferation of HLE cells. CONCLUSIONS: We report a yet unappreciated distinct expression pattern of clinically-relevant metabolic genes in HCC cell lines, which could enable the identification and therapeutic targeting of metabolic vulnerabilities at various liver cancer stages.

Authors: Z. C. Nwosu, N. Battello, M. Rothley, W. Pioronska, B. Sitek, M. P. Ebert, U. Hofmann, J. Sleeman, S. Wolfl, C. Meyer, D. A. Megger, S. Dooley

Date Published: 5th Sep 2018

Publication Type: Not specified

Abstract

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Authors: M Han, ZC Nwosu, MP Ebert, S Hammad, S Dooley, C Meyer

Date Published: 2019

Publication Type: Not specified

Abstract

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Authors: Silvia Colucci, Sandro Altamura, Oriana Marques, Anne Dropmann, Natalie K. Horvat, Katja Müdder, Seddik Hammad, Steven Dooley, Martina U. Muckenthaler

Date Published: 13th May 2021

Publication Type: Journal

Abstract (Expand)

When non-linear models are fitted to experimental data, parameter estimates can be poorly constrained albeit being identifiable in principle. This means that along certain paths in parameter space, the log-likelihood does not exceed a given statistical threshold but remains bounded. This situation, denoted as practical non-identifiability, can be detected by Monte Carlo sampling or by systematic scanning using the profile likelihood method. In contrast, any method based on a Taylor expansion of the log-likelihood around the optimum, e.g., parameter uncertainty estimation by the Fisher Information Matrix, reveals no information about the boundedness at all. In this work, we present a geometric approach, approximating the original log-likelihood by geodesic coordinates of the model manifold. The Christoffel Symbols in the geodesic equation are fixed to those obtained from second order model sensitivities at the optimum. Based on three exemplary non-linear models we show that the information about the log-likelihood bounds and flat parameter directions can already be contained in this local information. Whereas the unbounded case represented by the Fisher Information Matrix is embedded in the geometric framework as vanishing Christoffel Symbols, non-vanishing constant Christoffel Symbols prove to define prototype non-linear models featuring boundedness and flat parameter directions of the log-likelihood. Finally, we investigate if those models could allow to approximate and replace computationally expensive objective functions originating from non-linear models by a surrogate objective function in parameter estimation problems.

Authors: Daniel Lill, Jens Timmer, Daniel Kaschek

Date Published: 3rd Jun 2019

Publication Type: Not specified

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