Edda Klipp

About Edda Klipp:
No description specified

SEEK ID: https://seek.lisym.org/people/143

Location:  Germany

Expertise: Mathematical Modelling, Systems Biology, Data-driven ODE modeling, Biophysics

Tools: Cytometry and fluorescent microscopy, FBA, Dynamic modeling/dMod, Computational and theoretical biology, Partial differential equations, kinetic modelling, live cell imaging, Bifurcation analysis, Mathematical Modelling, qPCR, Algebraic equations

ORCID: https://orcid.org/0000-0002-0567-7075

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LiSyM-Krebs - Systemmedizinisches Forschungsnetz zur Früherkennung und Prävention von Leberkrebs

LiSyM-Krebs ist ein nationales Forschungsnetz zur Früherkennung und Prävention von Leberkrebs, das unter Verwendung des systemmedizinischen Forschungsansatzes die komplexen, dynamischen Prozesse der Krankheitsprogression analysiert, um ausgehend von den Erkenntnissen aus dem Forschungsnetz LiSyM die Entstehung von Leberkrebs besser zu verstehen, vorherzusagen und im besten Fall sogar zu verhindern. LiSyM-Krebs setzt die erfolgreichen Forschungsaktivitäten der BMBF-Vorgängerprogramme ...

Projects: LiSyM-Krebs Partnering, Forschungsnetzwerk LiSyM-Krebs, SMART-NAFLD, DEEP-HCC network, C-TIP-HCC network

Web page: Not specified

LiSyM: Liver Systems Medicine

Liver Systems Medicine : striving to develop non-invasive methods for diagnosing and treating NAFLD by combining mathematical modeling and biological research. LiSyM, is a multidisciplinary research network, in which molecular and cell biologists, clinical researchers, pharmacologists and experts in mathematical modeling examine the liver in its entirety. LiSyM research focuses on the metabolic liver disease non-alcoholic fatty liver disease (NAFLD), which includes non-alcoholic steatohepatitis ...

Projects: LiSyM Core Infrastructure and Management (LiSyM-PD), LiSyM Pillar I: Early Metabolic Injury (LiSyM-EMI), LiSyM Pillar II: Chronic Liver Disease Progression (LiSyM-DP), LiSyM Pillar III: Regeneration and Repair in Acute-on-Chronic Liver Failure (LiSyM-ACLF), LiSyM Pillar IV: Liver Function Diagnostics (LiSyM-LiFuDi), Model Guided Pharmacotherapy In Chronic Liver Disease (LiSyM-MGP), Molecular Steatosis - Imaging & Modeling (LiSyM-MSIM), The Hedgehog Signalling Pathway (LiSyM-JGMMS), Multi-Scale Models for Personalized Liver Function Tests (LiSyM-MM-PLF), LiSyM PALs, Project Management PTJ, LiSyM network, LiSyM Scientific Leadership Team (LiSyM-LT)

Web page: https://www.lisym.org/

LiSyM Pillar I: Early Metabolic Injury (LiSyM-EMI)

One of the tasks of the healthy liver is to store fat. Yet, at some stage, too much fat makes the liver sick. One critical time point occurs when a healthy fatty liver becomes inflamed and progresses to steatohepatitis, or NASH. LiSyM-Pillar I will identify what events lead to this transition. Does it occur in all parts of the liver? Which molecules indicate that it is taking place? Can the degeneration be stopped or undone - and if so, how?

Programme: LiSyM: Liver Systems Medicine

Public web page: http://www.lisym.org/our-work/pillar-research/zones-of-the-liver

Start date: 1st Jan 2016

LiSyM network

This comprises the whole LiSyM network

Programme: LiSyM: Liver Systems Medicine

Public web page: http://www.lisym.org

Start date: 1st Jan 2016

LiSyM-Krebs Partnering

This generic project is intended to be a forum for all LiSyM partner and external stakeholders interested in participating in the BMBF initiative LiSyM-Krebs.

Programme: LiSyM-Krebs - Systemmedizinisches Forschungsnetz zur Früherkennung und Prävention von Leberkrebs

Public web page: https://www.ptj.de/projektfoerderung/gesundheitsforschung/lisym-krebs

Non-alcoholic fatty liver disease Stratification by Liver Lipidomics
LiSyM Pillar I: Early Metabolic Injury (LiSyM-EMI)

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

Loss of hepatic Mboat7 leads to liver fibrosis.
LiSyM Pillar I: Early Metabolic Injury (LiSyM-EMI), LiSyM network

Abstract (Expand)

OBJECTIVE: The rs641738C>T variant located near the membrane-bound O-acyltransferase domain containing 7 (MBOAT7) locus is associated with fibrosis in liver diseases, including non-alcoholic fatty … liver disease (NAFLD), alcohol-related liver disease, hepatitis B and C. We aim to understand the mechanism by which the rs641738C>T variant contributes to pathogenesis of NAFLD. DESIGN: Mice with hepatocyte-specific deletion of MBOAT7 (Mboat7(Deltahep)) were generated and livers were characterised by histology, flow cytometry, qPCR, RNA sequencing and lipidomics. We analysed the association of rs641738C>T genotype with liver inflammation and fibrosis in 846 NAFLD patients and obtained genotype-specific liver lipidomes from 280 human biopsies. RESULTS: Allelic imbalance analysis of heterozygous human liver samples pointed to lower expression of the MBOAT7 transcript on the rs641738C>T haplotype. Mboat7(Deltahep) mice showed spontaneous steatosis characterised by increased hepatic cholesterol ester content after 10 weeks. After 6 weeks on a high fat, methionine-low, choline-deficient diet, mice developed increased hepatic fibrosis as measured by picrosirius staining (p<0.05), hydroxyproline content (p<0.05) and transcriptomics, while the inflammatory cell populations and inflammatory mediators were minimally affected. In a human biopsied NAFLD cohort, MBOAT7 rs641738C>T was associated with fibrosis (p=0.004) independent of the presence of histological inflammation. Liver lipidomes of Mboat7(Deltahep) mice and human rs641738TT carriers with fibrosis showed increased total lysophosphatidylinositol levels. The altered lysophosphatidylinositol and phosphatidylinositol subspecies in MBOAT7(Deltahep) livers and human rs641738TT carriers were similar. CONCLUSION: Mboat7 deficiency in mice and human points to an inflammation-independent pathway of liver fibrosis that may be mediated by lipid signalling and a potentially targetable treatment option in NAFLD.

Authors: V. R. Thangapandi, O. Knittelfelder, M. Brosch, E. Patsenker, O. Vvedenskaya, S. Buch, S. Hinz, A. Hendricks, M. Nati, A. Herrmann, D. R. Rekhade, T. Berg, M. Matz-Soja, K. Huse, E. Klipp, J. K. Pauling, J. A. Wodke, J. Miranda Ackerman, M. V. Bonin, E. Aigner, C. Datz, W. von Schonfels, S. Nehring, S. Zeissig, C. Rocken, A. Dahl, T. Chavakis, F. Stickel, A. Shevchenko, C. Schafmayer, J. Hampe, P. Subramanian

Date Published: 26th Jun 2020

Publication Type: Journal

Geospatial precision simulations of community confined human interactions during SARS-CoV-2 transmission reveals bimodal intervention outcomes
LiSyM Pillar I: Early Metabolic Injury (LiSyM-EMI), LiSyM network

Abstract

Not specified

Authors: Bjoern Goldenbogen, Stephan O. Adler, Oliver Bodeit, Judith AH Wodke, Aviv Korman, Lasse Bonn, Ximena Martinez de la Escalera, Johanna E L Haffner, Maria Krantz, Maxim Karnetzki, Ivo Maintz, Lisa Mallis, Rafael U Moran Torres, Hannah Prawitz, Patrick Segelitz, Martin Seeger, Rune Linding, Edda Klipp

Date Published: 6th May 2020

Publication Type: Unpublished

MEMOTE for standardized genome-scale metabolic model testing.
Multi-Scale Models for Personalized Liver Function Tests (LiSyM-MM-PLF)

Abstract

Not specified

Authors: C. Lieven, M. E. Beber, B. G. Olivier, F. T. Bergmann, M. Ataman, P. Babaei, J. A. Bartell, L. M. Blank, S. Chauhan, K. Correia, C. Diener, A. Drager, B. E. Ebert, J. N. Edirisinghe, J. P. Faria, A. M. Feist, G. Fengos, R. M. T. Fleming, B. Garcia-Jimenez, V. Hatzimanikatis, W. van Helvoirt, C. S. Henry, H. Hermjakob, M. J. Herrgard, A. Kaafarani, H. U. Kim, Z. King, S. Klamt, E. Klipp, J. J. Koehorst, M. Konig, M. Lakshmanan, D. Y. Lee, S. Y. Lee, S. Lee, N. E. Lewis, F. Liu, H. Ma, D. Machado, R. Mahadevan, P. Maia, A. Mardinoglu, G. L. Medlock, J. M. Monk, J. Nielsen, L. K. Nielsen, J. Nogales, I. Nookaew, B. O. Palsson, J. A. Papin, K. R. Patil, M. Poolman, N. D. Price, O. Resendis-Antonio, A. Richelle, I. Rocha, B. J. Sanchez, P. J. Schaap, R. S. Malik Sheriff, S. Shoaie, N. Sonnenschein, B. Teusink, P. Vilaca, J. O. Vik, J. A. H. Wodke, J. C. Xavier, Q. Yuan, M. Zakhartsev, C. Zhang

Date Published: 4th Mar 2020

Publication Type: Journal

Computational Lipidomics and Lipid Bioinformatics: Filling In the Blanks.
LiSyM Pillar I: Early Metabolic Injury (LiSyM-EMI)

Abstract (Expand)

Lipids are highly diverse metabolites of pronounced importance in health and disease. While metabolomics is a broad field under the omics umbrella that may also relate to lipids, lipidomics is an … emerging field which specializes in the identification, quantification and functional interpretation of complex lipidomes. Today, it is possible to identify and distinguish lipids in a high-resolution, high-throughput manner and simultaneously with a lot of structural detail. However, doing so may produce thousands of mass spectra in a single experiment which has created a high demand for specialized computational support to analyze these spectral libraries. The computational biology and bioinformatics community has so far established methodology in genomics, transcriptomics and proteomics but there are many (combinatorial) challenges when it comes to structural diversity of lipids and their identification, quantification and interpretation. This review gives an overview and outlook on lipidomics research and illustrates ongoing computational and bioinformatics efforts. These efforts are important and necessary steps to advance the lipidomics field alongside analytic, biochemistry, biomedical and biology communities and to close the gap in available computational methodology between lipidomics and other omics sub-branches.

Authors: J. Pauling, E. Klipp

Date Published: 22nd Dec 2016

Publication Type: Not specified

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