Publications

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385 Publications visible to you, out of a total of 385

Abstract

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Authors: Adrien Guillot, Marc Winkler, Milessa Silva Afonso, Abhishek Aggarwal, David Lopez, Hilmar Berger, Marlene S. Kohlhepp, Hanyang Liu, Burcin Özdirik, Johannes Eschrich, Jing Ma, Moritz Peiseler, Felix Heymann, Swetha Pendem, Sangeetha Mahadevan, Bin Gao, Lauri Diehl, Ruchi Gupta, Frank Tacke

Date Published: 2023

Publication Type: Journal

Abstract

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Authors: Felix Heymann, Jana C. Mossanen, Moritz Peiseler, Patricia M. Niemietz, Bruna Araujo David, Oliver Krenkel, Anke Liepelt, Matheus Batista Carneiro, Marlene S. Kohlhepp, Paul Kubes, Frank Tacke

Date Published: 2023

Publication Type: Journal

Abstract

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Authors: Savneet Kaur, Srivatsan Kidambi, Martí Ortega-Ribera, Le Thi Thanh Thuy, Natalia Nieto, Victoria C. Cogger, Wei-Fen Xie, Frank Tacke, Jordi Gracia-Sancho

Date Published: 2023

Publication Type: Journal

Abstract

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Authors: Herbert Tilg, Timon E. Adolph, Frank Tacke

Date Published: 2023

Publication Type: Journal

Abstract

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Authors: Joscha Vonderlin, Triantafyllos Chavakis, Michael Sieweke, Frank Tacke

Date Published: 2023

Publication Type: Journal

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Authors: Sai Wang, Frederik Link, Rilu Feng, Stefan Munker, Yujia Li, Roman Liebe, Matthias P. Ebert, Steven Dooley, Huiguo Ding, Shanshan Wang, Honglei Weng

Date Published: 2023

Publication Type: Journal

Abstract

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Authors: Alaa Hammad, Seddik Hammad, Kerry Gould, Matthias P. Ebert, Steven Dooley, Anne Dropmann

Date Published: 2023

Publication Type: Journal

Abstract

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Authors: Seddik Hammad, Christoph Ogris, Amnah Othman, Pia Erdoesi, Wolfgang Schmidt-Heck, Ina Biermayer, Barbara Helm, Yan Gao, Weronika Piorońska, Lorenza D'Alessandro, Fabian J. Theis, Matthias P. Ebert, Ursula Klingmüller, Jan Hengstler, Nikola S. Mueller, Steven Dooley

Date Published: 2023

Publication Type: Journal

Abstract

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Authors: Rilu Feng, Kejia Kan, Carsten Sticht, Yujia Li, Shanshan Wang, Hui Liu, Chen Shao, Stefan Munker, Hanno Niess, Sai Wang, Christoph Meyer, Roman Liebe, Matthias P. Ebert, Steven Dooley, Huiguo Ding, Honglei Weng

Date Published: 1st Dec 2022

Publication Type: Journal

Abstract (Expand)

MOTIVATION: Over the last decades, image processing and analysis have become one of the key technologies in systems biology and medicine. The quantification of anatomical structures and dynamic processes in living systems is essential for understanding the complex underlying mechanisms and allows, i.e. the construction of spatio-temporal models that illuminate the interplay between architecture and function. Recently, deep learning significantly improved the performance of traditional image analysis in cases where imaging techniques provide large amounts of data. However, if only a few images are available or qualified annotations are expensive to produce, the applicability of deep learning is still limited. RESULTS: We present a novel approach that combines machine learning-based interactive image segmentation using supervoxels with a clustering method for the automated identification of similarly colored images in large image sets which enables a guided reuse of interactively trained classifiers. Our approach solves the problem of deteriorated segmentation and quantification accuracy when reusing trained classifiers which is due to significant color variability prevalent and often unavoidable in biological and medical images. This increase in efficiency improves the suitability of interactive segmentation for larger image sets, enabling efficient quantification or the rapid generation of training data for deep learning with minimal effort. The presented methods are applicable for almost any image type and represent a useful tool for image analysis tasks in general. AVAILABILITY AND IMPLEMENTATION: The presented methods are implemented in our image processing software TiQuant which is freely available at tiquant.hoehme.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Authors: A. Friebel, T. Johann, D. Drasdo, S. Hoehme

Date Published: 30th Sep 2022

Publication Type: Journal

Abstract (Expand)

Erythropoietin (Epo) ensures survival and proliferation of colony-forming unit erythroid (CFU-E) progenitor cells and their differentiation to hemoglobin-containing mature erythrocytes. A lack of Epo-induced responses causes embryonic lethality, but mechanisms regulating the dynamic communication of cellular alterations to the organismal level remain unresolved. By time-resolved transcriptomics and proteomics, we show that Epo induces in CFU-E cells a gradual transition from proliferation signature proteins to proteins indicative for differentiation, including heme-synthesis enzymes. In the absence of the Epo receptor (EpoR) in embryos, we observe a lack of hemoglobin in CFU-E cells and massive iron overload of the fetal liver pointing to a miscommunication between liver and placenta. A reduction of iron-sulfur cluster-containing proteins involved in oxidative phosphorylation in these embryos leads to a metabolic shift toward glycolysis. This link connecting erythropoiesis with the regulation of iron homeostasis and metabolic reprogramming suggests that balancing these interactions is crucial for protection from iron intoxication and for survival.

Authors: S. Chakraborty, G. Andrieux, P. Kastl, L. Adlung, S. Altamura, M. E. Boehm, L. E. Schwarzmuller, Y. Abdullah, M. C. Wagner, B. Helm, H. J. Grone, W. D. Lehmann, M. Boerries, H. Busch, M. U. Muckenthaler, M. Schilling, U. Klingmuller

Date Published: 20th Sep 2022

Publication Type: Journal

Abstract (Expand)

The characterization of novel radiotracers toward their metabolic stability is an essential part of their development. While in vitro methods such as liver microsome assays or ex vivo blood or tissue samples provide information on overall stability, little or no information is obtained on cytochrome P450 (CYP) enzyme and isoform-specific contribution to the metabolic fate of individual radiotracers. Herein, we investigated recently established CYP-overexpressing hepatoblastoma cell lines (HepG2) for their suitability to study the metabolic stability of radiotracers in general and to gain insight into CYP isoform specificity. Wildtype HepG2 and CYP1A2-, CYP2C19-, and CYP3A4-overexpressing HepG2 cells were incubated with radiotracers, and metabolic turnover was analyzed. The optimized protocol, covering cell seeding in 96-well plates and analysis of supernatant by radio thin-layer-chromatography for higher throughput, was transferred to the evaluation of three (18)F-labeled celecoxib-derived cyclooxygenase-2 inhibitors (coxibs). These investigations revealed time-dependent degradation of the intact radiotracers, as well as CYP isoform- and substrate-specific differences in their metabolic profiles. HepG2 CYP2C19 proved to be the cell line showing the highest metabolic turnover for each radiotracer studied here. Comparison with human and murine liver microsome assays showed good agreement with the human metabolite profile obtained by the HepG2 cell lines. Therefore, CYP-overexpressing HepG2 cells provide a good complement for assessing the metabolic stability of radiotracers and allow the analysis of the CYP isoform-specific contribution to the overall radiotracer metabolism.

Authors: S. Lemm, S. Kohler, R. Wodtke, F. Jung, J. H. Kupper, J. Pietzsch, M. Laube

Date Published: 7th Aug 2022

Publication Type: Journal

Abstract (Expand)

Objectives. We assessed the potential of glial cell line-derived neurotrophic factor (GDNF) as a useful biomarker to predict cirrhosis in chronic hepatitis B (CHB) patients. Methods. A total of 735nts. Methods. A total of 735 patients from two medical centers (385 CHB patients and 350 healthy controls) were included to determine the association of serum and tissue GDNF levels with biopsy-proven cirrhosis. The diagnostic accuracy of serum GDNF (sGDNF) was estimated and compared with other indices of cirrhosis. Results. We showed significantly higher levels of sGDNF in CHB patients with fibrosis (28.4 pg/ml vs. 11.6 pg/ml in patients without) and patients with cirrhosis (33.8 pg/ml vs. 23.5 pg/ml in patients without). The areas under receiver operating curve (AUROCs) of sGDNF were 0.83 (95% confidence interval (CI): 0.80–0.87) for predicting liver fibrosis and 0.84 (95% CI: 0.79–0.89) for cirrhosis. Findings from the serum protein level and hepatic mRNA expression were consistent. Using the best cutoff to predict cirrhosis, we categorized the patients into sGDNF-high and sGDNF-low groups. The sGDNF-high group had significantly larger Masson’s trichrome and reticulin staining-positive area, higher Scheuer score, and METAVIR fibrosis stage (all p < 0.001 ) but not steatosis. On multivariable regression, sGDNF was independently associated with cirrhosis with an odds ratio of 6.98 (95% CI: 1.10–17.94). Finally, we demonstrated that sGDNF outperformed AST to platelet ratio index, FIB-4, fibroscore, forn index, and fibrometer in differentiating F4 vs. F3. Conclusion. Using serum, tissue mRNA, and biopsy data, our study revealed a significant potential of sGDNF as a novel noninvasive biomarker for cirrhosis in CHB patients.

Authors: Guangyue Yang, Liping Zhuang, Tiantian Sun, Yee Hui Yeo, Le Tao, Wei Zhang, Wenting Ma, Liu Wu, Zongguo Yang, Yanqin Yang, Dongying Xue, Jie Zhang, Rilu Feng, Ebert Matthias P., Steven Dooley, Ekihiro Seki, Ping Liu, Cheng Liu

Date Published: 9th Jul 2022

Publication Type: Journal

Abstract (Expand)

The hepatic Na+-taurocholate cotransporting polypeptide NTCP/SLC10A1 is important for the uptake of bile salts and selected drugs. Its inhibition results in increased systemic bile salt concentrations.oncentrations. NTCP is also the entry receptor for the hepatitis B/D virus. We investigated interindividual hepatic SLC10A1/NTCP expression using various omics technologies. SLC10A1/NTCP mRNA expression/protein abundance was quantified in well-characterized 143 human livers by real-time PCR and LC-MS/MS-based targeted proteomics. Genome-wide SNP arrays and SLC10A1 next-generation sequencing were used for genomic analyses. SLC10A1 DNA methylation was assessed through MALDI-TOF MS. Transcriptomics and untargeted metabolomics (UHPLC-Q-TOF-MS) were correlated to identify NTCP-related metabolic pathways. SLC10A1 mRNA and NTCP protein levels varied 44-fold and 10.4-fold, respectively. Non-genetic factors (e.g., smoking, alcohol consumption) influenced significantly NTCP expression. Genetic variants in SLC10A1 or other genes do not explain expression variability which was validated in livers (n = 50) from The Cancer Genome Atlas. The identified two missense SLC10A1 variants did not impair transport function in transfectants. Specific CpG sites in SLC10A1 as well as single metabolic alterations and pathways (e.g., peroxisomal and bile acid synthesis) were significantly associated with expression. Inter-individual variability of NTCP expression is multifactorial with the contribution of clinical factors, DNA methylation, transcriptional regulation as well as hepatic metabolism, but not genetic variation.

Authors: Roman Tremmel, Anne T. Nies, Barbara A. C. van Eijck, Niklas Handin, Mathias Haag, Stefan Winter, Florian A. Büttner, Charlotte Kölz, Franziska Klein, Pascale Mazzola, Ute Hofmann, Kathrin Klein, Per Hoffmann, Markus M. Nöthen, Fabienne Z. Gaugaz, Per Artursson, Matthias Schwab, Elke Schaeffeler

Date Published: 1st Jul 2022

Publication Type: Journal

Abstract (Expand)

In health and disease, liver cells are continuously exposed to cytokines and growth factors. While individual signal transduction pathways induced by these factors were studied in great detail, the cellular responses induced by repeated or combined stimulations are complex and less understood. Growth factor receptors on the cell surface of hepatocytes were shown to be regulated by receptor interactions, receptor trafficking and feedback regulation. Here, we exemplify how mechanistic mathematical modelling based on quantitative data can be employed to disentangle these interactions at the molecular level. Crucial is the analysis at a mechanistic level based on quantitative longitudinal data within a mathematical framework. In such multi-layered information, step-wise mathematical modelling using submodules is of advantage, which is fostered by sharing of standardized experimental data and mathematical models. Integration of signal transduction with metabolic regulation in the liver and mechanistic links to translational approaches promise to provide predictive tools for biology and personalized medicine.

Authors: Lorenza A. D’Alessandro, Ursula Klingmüller, Marcel Schilling

Date Published: 30th Jun 2022

Publication Type: Journal

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