ORCID Profile
0000-0001-6286-1138
Current Organisation
Institut Pasteur
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Publisher: Cold Spring Harbor Laboratory
Date: 28-04-2023
DOI: 10.1101/2023.04.26.538374
Abstract: Hepatic steatosis is the result of an imbalance between nutrient delivery and metabolism in the liver. It is the first hallmark of Non-alcoholic fatty liver disease (NAFLD) and is characterized by the accumulation of excess lipids in the liver that can drive liver failure, inflammation, and cancer. Mitochondria control the fate and function of cells and compelling evidence implicates these multifunctional organelles in the appearance and progression of liver dysfunction, although it remains to be elucidated which specific mitochondrial functions are actually causally linked to NAFLD. Here, we identified Mitochondrial Fission Process 1 protein (MTFP1) as a key regulator of mitochondrial and metabolic activity in the liver. Deletion of Mtfp1 in hepatocytes is physiologically benign in mice yet leads to the upregulation of oxidative phosphorylation (OXPHOS) activity and mitochondrial respiration, independently of mitochondrial biogenesis. Consequently, hepatocyte-specific knockout mice are protected against high fat diet-induced hepatic steatosis and metabolic dysregulation. Additionally, we find that deletion of Mtfp1 in liver mitochondria inhibits mitochondrial permeability transition pore opening in hepatocytes, conferring protection against apoptotic liver damage in vivo and ex vivo. Our work uncovers novel functions of MTFP1 in the liver, positioning this gene as an unexpected regulator of OXPHOS and a therapeutic candidate for NAFLD.
Publisher: Springer Science and Business Media LLC
Date: 22-02-2016
DOI: 10.1038/NMETH.3773
Publisher: American Physical Society (APS)
Date: 12-05-2005
Publisher: American Physical Society (APS)
Date: 22-11-2005
Publisher: Cold Spring Harbor Laboratory
Date: 20-07-2017
DOI: 10.1101/166223
Abstract: Large pharmacogenomic screenings integrate heterogeneous cancer genomic data sets as well as anti-cancer drug responses on thousand human cancer cell lines. Mining this data to identify new therapies for cancer sub-populations would benefit from common data structures, modular computational biology tools and user-friendly interfaces. We have developed GDSCTools: a software aimed at the identification of clinically relevant genomic markers of drug response. The Genomics of Drug Sensitivity in Cancer (GDSC) database ( www.cancerRxgene.org ) integrates heterogeneous cancer genomic data sets as well as anti-cancer drug responses on a thousand cancer cell lines. Including statistical tools (ANOVA) and predictive methods (Elastic Net), as well as common data structures, GDSCTools allows users to reproduce published results from GDSC, to analyse their own drug responses or genomic datasets, and to implement new analytical methods. thomas.cokelaer@pasteur.fr
Publisher: F1000 Research Ltd
Date: 08-04-2016
DOI: 10.12688/F1000RESEARCH.7118.2
Abstract: DREAM challenges are community competitions designed to advance computational methods and address fundamental questions in system biology and translational medicine. Each challenge asks participants to develop and apply computational methods to either predict unobserved outcomes or to identify unknown model parameters given a set of training data. Computational methods are evaluated using an automated scoring metric, scores are posted to a public leaderboard, and methods are published to facilitate community discussions on how to build improved methods. By engaging participants from a wide range of science and engineering backgrounds, DREAM challenges can comparatively evaluate a wide range of statistical, machine learning, and biophysical methods. Here, we describe DREAMTools , a Python package for evaluating DREAM challenge scoring metrics. DREAMTools provides a command line interface that enables researchers to test new methods on past challenges, as well as a framework for scoring new challenges. As of March 2016, DREAMTools includes more than 80% of completed DREAM challenges. DREAMTools complements the data, metadata, and software tools available at the DREAM website dreamchallenges.org and on the Synapse platform at www.synapse.org . Availability: DREAMTools is a Python package. Releases and documentation are available at ypi/dreamtools . The source code is available at reamtools/dreamtools .
Publisher: F1000 Research Ltd
Date: 09-10-2015
DOI: 10.12688/F1000RESEARCH.7118.1
Abstract: DREAM challenges are community competitions designed to advance computational methods and address fundamental questions in system biology and translational medicine. Each challenge asks participants to develop and apply computational methods to either predict unobserved outcomes or to identify unknown model parameters given a set of training data. Computational methods are evaluated using an automated scoring metric, scores are posted to a public leaderboard, and methods are published to facilitate community discussions on how to build improved methods. By engaging participants from a wide range of science and engineering backgrounds, DREAM challenges can comparatively evaluate a wide range of statistical, machine learning, and biophysical methods. Here, we describe DREAMTools, a Python package for evaluating DREAM challenge scoring metrics. DREAMTools provides a command line interface that enables researchers to test new methods on past challenges, as well as a framework for scoring new challenges. As of September 2015, DREAMTools includes more than 80% of completed DREAM challenges. DREAMTools complements the data, metadata, and software tools available at the DREAM website dreamchallenges.org and on the Synapse platform www.synapse.org. Availability : DREAMTools is a Python package. Releases and documentation are available at ypi/dreamtools. The source code is available at reamtools.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Thomas Cokelaer.