Publications

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72 Publications visible to you, out of a total of 72
Modeling and Predicting Spatio-Temporal Dynamics of PM$_{2.5}$ Concentrations Through Time-Evolving Covariance Models

Abstract

Not specified

Authors: Ghulam A. Qadir, Ying Sun

Date Published: 2025

Publication Type: Journal

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The TGF-β1 target WISP1 is highly expressed in liver cirrhosis and cirrhotic HCC microenvironment and involved in pro- and anti-tumorigenic effects

Abstract

Not specified

Authors: Anne Dropmann, Sophie Alex, Katharina Schorn, Chenhao Tong, Tiziana Caccamo, Patricio Godoy, Iryna Ilkavets, Roman Liebe, Daniela Gonzalez, Jan G. Hengstler, Albrecht Piiper, Luca Quagliata, Matthias S. Matter, Oliver Waidmann, Fabian Finkelmeier, Teng Feng, Thomas S. Weiss, Nuh Rahbari, Emrullah Birgin, Erik Rasbach, Stephanie Roessler, Kai Breuhahn, Marcell Tóth, Matthias P. Ebert, Steven Dooley, Seddik Hammad, Nadja M. Meindl-Beinker

Date Published: 1st Nov 2024

Publication Type: Journal

Tumour-specific activation of a tumour-blood transport improves the diagnostic accuracy of blood tumour markers in mice

Abstract

Not specified

Authors: Christian Schmithals, Bianca Kakoschky, Dominic Denk, Maike von Harten, Jan Henrik Klug, Edith Hintermann, Anne Dropmann, Eman Hamza, Anne Claire Jacomin, Jens U. Marquardt, Stefan Zeuzem, Peter Schirmacher, Eva Herrmann, Urs Christen, Thomas J. Vogl, Oliver Waidmann, Steven Dooley, Fabian Finkelmeier, Albrecht Piiper

Date Published: 1st Jul 2024

Publication Type: Journal

Retinoic acid generates a beneficial microenvironment for liver progenitor cell activation in acute liver failure

Abstract (Expand)

Background: When massive necrosis occurs in acute liver failure (ALF), rapid expansion of HSCs called liver progenitor cells (LPCs) in a process called ductular reaction is required for survival. The …tular reaction is required for survival. The underlying mechanisms governing this process are not entirely known to date. In ALF, high levels of retinoic acid (RA), a molecule known for its pleiotropic roles in embryonic development, are secreted by activated HSCs. We hypothesized that RA plays a key role in ductular reaction during ALF. Methods: RNAseq was performed to identify molecular signaling pathways affected by all- trans retinoid acid (atRA) treatment in HepaRG LPCs. Functional assays were performed in HepaRG cells treated with atRA or cocultured with LX-2 cells and in the liver tissue of patients suffering from ALF. Results: Under ALF conditions, activated HSCs secreted RA, inducing RARα nuclear translocation in LPCs. RNAseq data and investigations in HepaRG cells revealed that atRA treatment activated the WNT-β-Catenin pathway, enhanced stemness genes (SOX9, AFP, and others), increased energy storage, and elevated the expression of ATP-binding cassette transporters in a RARα nuclear translocation-dependent manner. Further, atRA treatment–induced pathways were confirmed in a coculture system of HepaRG with LX-2 cells. Patients suffering from ALF who displayed RARα nuclear translocation in the LPCs had significantly better MELD scores than those without. Conclusions: During ALF, RA secreted by activated HSCs promotes LPC activation, a prerequisite for subsequent LPC-mediated liver regeneration.

Authors: Sai Wang, Frederik Link, Stefan Munker, Wenjing Wang, Rilu Feng, Roman Liebe, Yujia Li, Ye Yao, Hui Liu, Chen Shao, Matthias P.A. Ebert, Huiguo Ding, Steven Dooley, Hong-Lei Weng, Shan-Shan Wang

Date Published: 2024

Publication Type: Journal

The interplay of TGF-β1 and cholesterol orchestrating hepatocyte cell fate, EMT, and signals for HSC activation

Abstract (Expand)

Background and Aims: Transforming growth factor-β1 (TGF-β1) plays important roles in chronic liver diseases, including metabolic dysfunction-associated steatotic liver disease (MASLD). MASLD involves …SLD involves various biological processes including dysfunctional cholesterol metabolism and contributes to progression to metabolic dysfunction-associated steatohepatitis (MASH) and hepatocellular carcinoma (HCC). However, the reciprocal regulation of TGF-β1 signaling and cholesterol metabolism in MASLD is yet unknown. Methods: Changes in transcription of genes associated with cholesterol metabolism were assessed by RNA-Seq of murine hepatocyte cell line (AML12) and mouse primary hepatocytes (MPH) treated with TGF-β1. Functional assays were performed on AML12 cells (untreated, TGF-β1 treated, or subjected to cholesterol enrichment (CE) or depletion (CD)), and on mice injected with adeno-associated virus 8 (AAV8)-Control/TGF-β1. Results: TGF-β1 inhibited mRNA expression of several cholesterol metabolism regulatory genes, including rate-limiting enzymes of cholesterol biosynthesis in AML12 cells, MPHs, and AAV8-TGF-β1-treated mice. Total cholesterol levels and lipid droplet accumulation in AML12 cells and liver tissue were also reduced upon TGF-β1 treatment. Smad2/3 phosphorylation following 2 h TGF-β1 treatment persisted after CE or CD and was mildly increased following CD, while TGF-β1-mediated AKT phosphorylation (30 min) was inhibited by CE. Furthermore, CE protected AML12 cells from several effects mediated by 72 h incubation with TGF-β1, including EMT, actin polymerization, and apoptosis. CD mimicked the outcome of long term TGF- β1 administration, an effect that was blocked by an inhibitor of the type I TGF-β receptor. Additionally, the supernatant of CE- or CD-treated AML12 cells inhibited or promoted, respectively, the activation of LX-2 hepatic stellate cells. Conclusions: TGF-β1 inhibits cholesterol metabolism while cholesterol attenuates TGF-β1 downstream effects in hepatocytes.

Authors: Sai Wang, Frederik Link, Mei Han, Roohi Chaudhary, Anastasia Asimakopoulos, Roman Liebe, Ye Yao, Seddik Hammad, Anne Dropmann, Marinela Krizanac, Claudia Rubie, Laura Kim Feiner, Matthias Glanemann, Matthias Ebert, Ralf Weiskirchen, Yoav I Henis, Marcelo Ehrlich, Steven Dooley

Date Published: 15th Aug 2023

Publication Type: Journal

Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile

Abstract (Expand)

The quantity of data required to give a valid analysis grows exponentially as machine learning dimensionality increases. In a single experiment, microarrays or gene expression profiling assesses and … determines gene expression levels and patterns in various cell types or tissues. The advent of DNA microarray technology has enabled simultaneous intensive care of hundreds of gene expressions on a single chip, advancing cancer categorization. The most challenging aspect of categorization is working out many information points from many sources. The proposed approach uses microarray data to train deep learning algorithms on extracted features and then uses the Latent Feature Selection Technique to reduce classification time and increase accuracy. The feature-selection-based techniques will pick the important genes before classifying microarray data for cancer prediction and diagnosis. These methods improve classification accuracy by removing duplicate and superfluous information. The Artificial Bee Colony (ABC) technique of feature selection was proposed in this research using bone marrow PC gene expression data. The ABC algorithm, based on swarm intelligence, has been proposed for gene identification. The ABC has been used here for feature selection that generates a subset of features and every feature produced by the spectators, making this a wrapper-based feature selection system. This method’s main goal is to choose the fewest genes that are critical to PC performance while also increasing prediction accuracy. Convolutional Neural Networks were used to classify tumors without labelling them. Lung, kidney, and brain cancer datasets were used in the procedure’s training and testing stages. Using the cross-validation technique of k-fold methodology, the Convolutional Neural Network has an accuracy rate of 96.43%. The suggested research includes techniques for preprocessing and modifying gene expression data to enhance future cancer detection accuracy.

Author: Hatim Z Almarzouki

Date Published: 2022

Publication Type: Journal

FAIR Computational Workflows

Abstract (Expand)

ional workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products. They can … inherently contribute to the FAIR data principles: by processing data according to established metadata; by creating metadata themselves during the processing of data; and by tracking and recording data provenance. These properties aid data quality assessment and contribute to secondary data usage. Moreover, workflows are digital objects in their own right. This paper argues that FAIR principles for workflows need to address their specific nature in terms of their composition of executable software steps, their provenance, and their development.

Authors: Carole Goble, Sarah Cohen-Boulakia, Stian Soiland-Reyes, Daniel Garijo, Yolanda Gil, Michael R. Crusoe, Kristian Peters, Daniel Schober

Date Published: 2020

Publication Type: Journal

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