Publications

293 Publications visible to you, out of a total of 293

Abstract

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Authors: Lenka Belicova, Urska Repnik, Julien Delpierre, Elzbieta Gralinska, Sarah Seifert, José Ignacio Valenzuela, Hernán Andrés Morales-Navarrete, Christian Franke, Helin Räägel, Evgeniya Shcherbinina, Tatiana Prikazchikova, Victor Koteliansky, Martin Vingron, Yannis L. Kalaidzidis, Timofei Zatsepin, Marino Zerial

Date Published: 4th Oct 2021

Publication Type: Journal

Abstract

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Authors: Christian H. Holland, Ricardo O. Ramirez Flores, Maiju Myllys, Reham Hassan, Karolina Edlund, Ute Hofmann, Rosemarie Marchan, Cristina Cadenas, Jörg Reinders, Stefan Hoehme, Abdel‐latif Seddek, Steven Dooley, Verena Keitel, Patricio Godoy, Brigitte Begher‐Tibbe, Christian Trautwein, Christian Rupp, Sebastian Mueller, Thomas Longerich, Jan G. Hengstler, Julio Saez‐Rodriguez, Ahmed Ghallab

Date Published: 28th Aug 2021

Publication Type: Journal

Abstract (Expand)

Non-alcoholic fatty liver disease (NAFLD) is a common metabolic dysfunction leading to hepatic steatosis. However, NAFLD's global impact on the liver lipidome is poorly understood. Using high-resolution shotgun mass spectrometry, we quantified the molar abundance of 316 species from 22 major lipid classes in liver biopsies of 365 patients, including non-steatotic patients with normal or excessive weight, patients diagnosed with NAFL (non-alcoholic fatty liver) or NASH (non-alcoholic steatohepatitis), and patients bearing common mutations of NAFLD-related protein factors. We confirmed the progressive accumulation of di- and tri- acylglycerols and cholesteryl esters in the liver of NAFL and NASH patients, while the bulk composition of glycerophospho- and sphingolipids remained unchanged. Further stratification by biclustering analysis identified sphingomyelin species comprising n24:2 fatty acid moieties as membrane lipid markers of NAFLD. Normalized relative abundance of sphingomyelins SM 43:3;2 and SM 43:1;2 containing n24:2 and n24:0 fatty acid moieties, respectively, showed opposite trends during NAFLD progression and distinguished NAFL and NASH lipidomes from the lipidome of non-steatoic livers. Together with several glycerophospholipids containing a C22:6 fatty acid moiety, these lipids serve as markers of early and advanced stages of NAFL.

Authors: Olga Vvedenskaya, Tim Daniel Rose, Oskar Knittelfelder, Alessandra Palladini, Judith Andrea Heidrun Wodke, Kai Schumann, Jacobo Miranda Ackerman, Yuting Wang, Canan Has, Mario Brosch, Veera Raghavan Thangapandi, Stephan Buch, Thomas Züllig, Jürgen Hartler, Harald C. Köfeler, Christoph Röcken, Ünal Coskun, Edda Klipp, Witigo von Schoenfels, Justus Gross, Clemens Schafmayer, Jochen Hampe, Josch Konstantin Pauling, Andrej Shevchenko

Date Published: 1st Aug 2021

Publication Type: Journal

Abstract

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Authors: Nachiket Vartak, Dirk Drasdo, Fabian Geisler, Tohru Itoh, Ronald P.J. Oude Elferink, Stan F.J. van de Graaf, John Chiang, Verena Keitel, Michael Trauner, Peter Jansen, Jan G Hengstler

Date Published: 23rd Jun 2021

Publication Type: Journal

Abstract (Expand)

Physiologically based pharmacokinetic (PBPK) models have been proposed as a tool for more accurate individual pharmacokinetic (PK) predictions and model-informed precision dosing, but their application in clinical practice is still rare. This study systematically assesses the benefit of using individual patient information to improve PK predictions. A PBPK model of caffeine was stepwise personalized by using individual data on (1) demography, (2) physiology, and (3) cytochrome P450 (CYP) 1A2 phenotype of 48 healthy volunteers participating in a single-dose clinical study. Model performance was benchmarked against a caffeine base model simulated with parameters of an average individual. In the first step, virtual twins were generated based on the study subjects' demography (height, weight, age, sex), which implicated the rescaling of average organ volumes and blood flows. The accuracy of PK simulations improved compared with the base model. The percentage of predictions within 0.8-fold to 1.25-fold of the observed values increased from 45.8% (base model) to 57.8% (Step 1). However, setting physiological parameters (liver blood flow determined by magnetic resonance imaging, glomerular filtration rate, hematocrit) to measured values in the second step did not further improve the simulation result (59.1% in the 1.25-fold range). In the third step, virtual twins matching individual demography, physiology, and CYP1A2 activity considerably improved the simulation results. The percentage of data within the 1.25-fold range was 66.15%. This case study shows that individual PK profiles can be predicted more accurately by considering individual attributes and that personalized PBPK models could be a valuable tool for model-informed precision dosing approaches in the future.

Authors: Rebekka Fendt, Ute Hofmann, Annika Schneider, Elke Schaeffeler, Rolf Burghaus, Ali Yilmaz, Lars Mathias Blank, Reinhold Kerb, Jan-Frederik Schlender, Matthias Schwab, Lars Kuepfer

Date Published: 30th May 2021

Publication Type: Journal

Abstract (Expand)

COVID-19 poses a major challenge to individuals and societies around the world. Yet, it is difficult to obtain a good overview of studies across different medical fields of research such as clinical trials, epidemiology, and public health. Here, we describe a consensus metadata model to facilitate structured searches of COVID-19 studies and resources along with its implementation in three linked complementary web-based platforms. A relational database serves as central study metadata hub that secures compatibilities with common trials registries (e.g. ICTRP and standards like HL7 FHIR, CDISC ODM, and DataCite). The Central Search Hub was developed as a single-page application, the other two components with additional frontends are based on the SEEK platform and MICA, respectively. These platforms have different features concerning cohort browsing, item browsing, and access to documents and other study resources to meet divergent user needs. By this we want to promote transparent and harmonized COVID-19 research.

Authors: C. O. Schmidt, J. Darms, A. Shutsko, M. Lobe, R. Nagrani, B. Seifert, B. Lindstadt, M. Golebiewski, S. Koleva, T. Bender, C. R. Bauer, U. Sax, X. Hu, M. Lieser, V. Junker, S. Klopfenstein, A. Zeleke, D. Waltemath, I. Pigeot, J. Fluck

Date Published: 27th May 2021

Publication Type: Journal

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)

Fibroblast growth factor 23 (FGF23), a hormone generally derived from bone, is important in phosphate and vitamin D homeostasis. In acute kidney injury (AKI) patients, high-circulating FGF23 levels are associated with disease progression and mortality. However, the organ and cell type of FGF23 production in AKI and the molecular mechanism of its excessive production are still unidentified. For insight, we investigated folic acid (FA)-induced AKI in mice. Interestingly, simultaneous with FGF23, orphan nuclear receptor ERR-γ expression is increased in the liver of FA-treated mice, and ectopic overexpression of ERR-γ was sufficient to induce hepatic FGF23 production. In patients and in mice, AKI is accompanied by up-regulated systemic IL-6, which was previously identified as an upstream regulator of ERR-γ expression in the liver. Administration of IL-6 neutralizing antibody to FA-treated mice or of recombinant IL-6 to healthy mice confirms IL-6 as an upstream regulator of hepatic ERR-γ-mediated FGF23 production. A significant (<i>P</i> &lt; 0.001) interconnection between high IL-6 and FGF23 levels as a predictor of AKI in patients that underwent cardiac surgery was also found, suggesting the clinical relevance of the finding. Finally, liver-specific depletion of ERR-γ or treatment with an inverse ERR-γ agonist decreased hepatic FGF23 expression and plasma FGF23 levels in mice with FA-induced AKI. Thus, inverse agonist of ERR-γ may represent a therapeutic strategy to reduce adverse plasma FGF23 levels in AKI.

Authors: Kamalakannan Radhakrishnan, Yong-Hoon Kim, Yoon Seok Jung, Don-Kyu Kim, Soon-Young Na, Daejin Lim, Dong Hun Kim, Jina Kim, Hyung-Seok Kim, Hyon E Choy, Sung Jin Cho, In-Kyu Lee, Şamil Ayvaz, Stefanie Nittka, Danilo Fliser, Stefan J Schunk, Thimoteus Speer, Steven Dooley, Chul-Ho Lee, Hueng-Sik Choi

Date Published: 20th Apr 2021

Publication Type: Journal

Abstract (Expand)

Alzheimer's disease (AD) is frequently accompanied by progressing weight loss, correlating with mortality. Counter-intuitively, weight loss in old age might predict AD onset but obesity in midlife increases AD risk. Furthermore, AD is associated with diabetes-like alterations in glucose metabolism. Here, we investigated metabolic features of amyloid precursor protein overexpressing APP23 female mice modeling AD upon long-term challenge with high-sucrose (HSD) or high-fat diet (HFD). Compared to wild type littermates (WT), APP23 females were less prone to mild HSD-induced and considerable HFD-induced glucose tolerance deterioration, despite unaltered glucose tolerance during normal-control diet. Indirect calorimetry revealed increased energy expenditure and hyperactivity in APP23 females. Dietary interventions, especially HFD, had weaker effects on lean and fat mass gain, steatosis and adipocyte hypertrophy of APP23 than WT mice, as shown by (1)H-magnetic-resonance-spectroscopy, histological and biochemical analyses. Proteome analysis revealed differentially regulated expression of mitochondrial proteins in APP23 livers and brains. In conclusion, hyperactivity, increased metabolic rate, and global mitochondrial dysfunction potentially add up to the development of AD-related body weight changes in APP23 females, becoming especially evident during diet-induced metabolic challenge. These findings emphasize the importance of translating this metabolic phenotyping into human research to decode the metabolic component in AD pathogenesis.

Authors: S. Schreyer, N. Berndt, J. Eckstein, M. Mulleder, S. Hemmati-Sadeghi, C. Klein, B. Abuelnor, A. Panzel, D. Meierhofer, J. Spranger, B. Steiner, S. Brachs

Date Published: 16th Apr 2021

Publication Type: Journal

Abstract (Expand)

Liver macrophages (LMs) play a central role in acute and chronic liver pathologies. Investigation of these processes in humans as well as the development of diagnostic tools and new therapeutic strategies require in vitro models that closely resemble the in vivo situation. In our study, we sought to gain further insight into the role of LMs in different liver pathologies and into their characteristics after isolation from liver tissue. For this purpose, LMs were characterized in human liver tissue sections using immunohistochemistry and bioinformatic image analysis. Isolated cells were characterized in suspension using FACS analyses and in culture using immunofluorescence staining and laser scanning microscopy as well as functional assays. The majority of our investigated liver tissues were characterized by anti-inflammatory LMs which showed a homogeneous distribution and increased cell numbers in correlation with chronic liver injuries. In contrast, pro-inflammatory LMs appeared as temporary and locally restricted reactions. Detailed characterization of isolated macrophages revealed a complex disease dependent pattern of LMs consisting of pro- and anti-inflammatory macrophages of different origins, regulatory macrophages and monocytes. Our study showed that in most cases the macrophage pattern can be transferred in adherent cultures. The observed exceptions were restricted to LMs with pro-inflammatory characteristics.

Authors: Andrea Zimmermann, René Hänsel, Kilian Gemünden, Victoria Kegel-Hübner, Jonas Babel, Hendrik Bläker, Madlen Matz-Soja, Daniel Seehofer, Georg Damm

Date Published: 1st Apr 2021

Publication Type: Journal

Abstract (Expand)

Non-Alcoholic Fatty Liver Disease (NAFLD) is the most common type of chronic liver disease in developed nations, affecting around 25% of the population. Elucidating the factors causing NAFLD in individual patients to progress in different rates and to different degrees of severity, is a matter of active medical research. Here, we aim to provide evidence that the intra-hepatic heterogeneity of rheological, metabolic and tissue-regenerating capacities plays a central role in disease progression. We developed a generic mathematical model that constitutes the liver as ensemble of small liver units differing in their capacities to metabolize potentially cytotoxic free fatty acids (FFAs) and to repair FFA-induced cell damage. Transition from simple steatosis to more severe forms of NAFLD is described as self-amplifying process of cascading liver failure, which, to stop, depends essentially on the distribution of functional capacities across the liver. Model simulations provided the following insights: (1) A persistently high plasma level of FFAs is sufficient to drive the liver through different stages of NAFLD; (2) Presence of NAFLD amplifies the deleterious impact of additional tissue-damaging hits; and (3) Coexistence of non-steatotic and highly steatotic regions is indicative for the later occurrence of severe NAFLD stages.

Authors: H. G. Holzhutter, N. Berndt

Date Published: 5th Mar 2021

Publication Type: Journal

Abstract (Expand)

BACKGROUND & AIMS: Bacterial infections (BI) affect the natural course of cirrhosis and were suggested to be a landmark event marking the transition to the decompensated stage. Our specific aim was to evaluate the impact of BI on the natural history of compensated cirrhosis. METHODS: We analyzed 858 patients with cirrhosis, evaluated for the INCA trial (EudraCT 2013-001626-26) in 2 academic medical centers between February 2014 and May 2019. Only patients with previously compensated disease were included. They were divided into 4 groups: compensated without BI, compensated with BI, 1st decompensation without BI, and 1st decompensation with BI. RESULTS: About 425 patients (median 61 [53-69] years) were included in the final prospective analysis. At baseline, 257 patients were compensated (12 [4.7%] with BI), whereas 168 patients presented with their 1st decompensation (42 [25.0%] with BI). In patients who remained compensated MELD scores were similar in those with and without BI. Patients with their first decompensation and BI had higher MELD scores than those without BI. Amongst patients who remained compensated, BI had no influence on transplant-free survival, whereas patients with their 1st decompensation and concurrent BI had significantly reduced transplant-free survival as compared with those without BI. The development of BI or decompensation during follow-up had a greater impact on survival than each of these complications at baseline. CONCLUSIONS: In compensated patients with cirrhosis, the 1st decompensation associated to BI has worse survival than decompensation without BI. By contrast, BI without decompensation does not negatively impact survival of patients with compensated cirrhosis.

Authors: M. C. Reichert, C. Schneider, R. Greinert, M. Casper, F. Grunhage, A. Wienke, A. Zipprich, F. Lammert, C. Ripoll

Date Published: 1st Mar 2021

Publication Type: Journal

Abstract (Expand)

In this work, we introduce an entirely data-driven and automated approach to reveal disease-associated biomarker and risk factor networks from heterogeneous and high-dimensional healthcare data. Our workflow is based on Bayesian networks, which are a popular tool for analyzing the interplay of biomarkers. Usually, data require extensive manual preprocessing and dimension reduction to allow for effective learning of Bayesian networks. For heterogeneous data, this preprocessing is hard to automatize and typically requires domain-specific prior knowledge. We here combine Bayesian network learning with hierarchical variable clustering in order to detect groups of similar features and learn interactions between them entirely automated. We present an optimization algorithm for the adaptive refinement of such group Bayesian networks to account for a specific target variable, like a disease. The combination of Bayesian networks, clustering, and refinement yields low-dimensional but disease-specific interaction networks. These networks provide easily interpretable, yet accurate models of biomarker interdependencies. We test our method extensively on simulated data, as well as on data from the Study of Health in Pomerania (SHIP-TREND), and demonstrate its effectiveness using non-alcoholic fatty liver disease and hypertension as examples. We show that the group network models outperform available biomarker scores, while at the same time, they provide an easily interpretable interaction network.

Authors: A. K. Becker, M. Dorr, S. B. Felix, F. Frost, H. J. Grabe, M. M. Lerch, M. Nauck, U. Volker, H. Volzke, L. Kaderali

Date Published: 13th Feb 2021

Publication Type: Journal

Abstract (Expand)

Besides the liver, hepatitis C virus (HCV) infection also affects kidney allografts. The aim of this study was to longitudinally evaluate viscoelasticity changes in the liver and in kidney allografts in kidney transplant recipients (KTRs) with HCV infection after treatment with direct-acting antiviral agents (DAAs). Fifteen KTRs with HCV infection were treated with DAAs (daclatasvir and sofosbuvir) for 3 months and monitored at baseline, end of treatment (EOT), and 3 (FU1) and 12 (FU2) months after EOT. Shear-wave speed (SWS) and loss angle of the complex shear modulus (phi), reflecting stiffness and fluidity, respectively, were reconstructed from multifrequency magnetic resonance elastography data with tomoelastography post-processing. After virus elimination by DAAs, hepatic stiffness and fluidity decreased, while kidney allograft stiffness and fluidity increased compared with baseline (hepatic stiffness change at FU1: -0.14 m/s, p < 0.01, and at FU2: -0.11 m/s, p < 0.05; fluidity at FU1: -0.05 rad, p = 0.04 and unchanged at FU2: p = 0.20; kidney allograft stiffness change at FU1: +0.27 m/s, p = 0.01, and at FU2: +0.30 m/s, p < 0.01; fluidity at FU1 and FU2: +0.06 rad, p = 0.02). These results suggest the restoration of mechanically sensitive structures and functions in both organs. Tomoelastography can be used to monitor the therapeutic results of HCV treatment non-invasively on the basis of hepatic and renal viscoelastic parameters.

Authors: S. R. Marticorena Garcia, C. E. Althoff, M. Durr, F. Halleck, K. Budde, U. Grittner, C. Burkhardt, K. Johrens, J. Braun, T. Fischer, B. Hamm, I. Sack, J. Guo

Date Published: 1st Feb 2021

Publication Type: Journal

Abstract (Expand)

Macrophage migration inhibitory factor (MIF) is a pleiotropic inflammatory cytokine with anti-fibrotic properties in toxic liver injury models and anti-steatotic functions in non-alcoholic fatty liver disease (NAFLD) attributed to the CD74/AMPK signaling pathway. As NAFLD progression is associated with fibrosis, we studied MIF function during NAFLD-associated liver fibrogenesis in mice and men by molecular, histological and immunological methods in vitro and in vivo. After NASH diet feeding, hepatic Mif expression was strongly induced, an effect which was absent in Mif∆hep mice. In contrast to hepatotoxic fibrosis models, NASH diet-induced fibrogenesis was significantly abrogated in Mif−/− and Mif∆hep mice associated with a reduced accumulation of the pro-fibrotic type-I NKT cell subpopulation. In vitro, MIF skewed the differentiation of NKT cells towards the type-I subtype. In line with the murine results, expression of fibrosis markers strongly correlated with MIF, its receptors, and markers of NKT type-I cells in NASH patients. We conclude that MIF expression is induced during chronic metabolic injury in mice and men with hepatocytes representing the major source. In NAFLD progression, MIF contributes to liver fibrogenesis skewing NKT cell polarization toward a pro-fibrotic phenotype highlighting the complex, context-dependent role of MIF during chronic liver injury.

Authors: D. Heinrichs, E. F. Brandt, P. Fischer, Janine Koehncke, Theresa H. Wirtz, N. Guldiken, S. Djudjaj, P. Boor, D.Kroy, R. Weiskirchen, Richard Bucala, H.E. Wasmuth, P. Strnad, Christian Trautwein, J. Bernhagen, M. L. Berres

Date Published: 28th Jan 2021

Publication Type: Journal

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