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Cecilia Jönsson

Principal Research Engineer

My research focuses on chronic liver disease, particularly steatotic liver disease. I aim to understand the development and progression of these conditions at the molecular level and to identify biomarkers for their early detection.

Presentation

Molecular Mechanisms and Non-Invasive Diagnostic Approaches in Chronic Liver Disease

Overweight and obesity are the primary risk factors for developing the most common chronic liver disease, metabolic dysfunction-associated steatotic liver disease. When fat accumulates in the liver (steatosis), it can disrupt normal cellular function, leading to inflammation and the deposition of connective tissue, known as fibrosis. In some cases, this fibrosis progresses to irreversible scarring, resulting in liver cirrhosis. Cirrhosis can have potentially fatal outcomes, including liver-related complications and hepatocellular carcinoma.

 

The molecular mechanisms driving why only certain individuals with liver fat deposits progress to advanced liver disease remain unclear. Additionally, there is a lack of blood-based biomarkers for diagnosing steatotic liver disease. Current blood markers can only exclude advanced steatotic liver disease through calculation algorithms. Intermediate or elevated results require further investigation, including elastography (using magnetic resonance imaging or FibroScan) to evaluate liver fat percentage and stiffness, and possibly a liver biopsy.

 

We conduct several prospective studies on steatotic liver disease and cirrhosis, aiming to identify blood-based biomarkers. Using mass spectrometry, NMR, and antibody-based techniques, we seek circulating molecules that can indicate the degree of liver fat and fibrosis. Through both "untargeted" and "targeted" proteomics and metabolomics, we hope to find markers that will improve and simplify the diagnosis and prognosis of chronic liver disease.

 

To better understand molecular mechanisms in the liver, we have established an ex vivo model system for metabolic flux analysis. We cultivate thin slices of liver tissue and supply them with nutrients labeled with stable isotopes. Using mass spectrometry, we measure the isotope ratios and levels of various molecules within the tissue. These experimental data, combined with mathematical modeling then allow us to calculate nutrient flow and enzymatic activity in liver tissue.

Publications

2024

Nina Grankvist, Cecilia Jönsson, Karin Hedin, Nicolas Sundqvist, Per Sandström, Bergthor Björnsson, Arjana Begzati, Evgeniya Mickols, Per Artursson, Mohit Jain, Gunnar Cedersund, Roland Nilsson (2024) Global 13C tracing and metabolic flux analysis of intact human liver tissue ex vivo Nature Metabolism (Article in journal) Continue to DOI
Cecilia Jönsson, Martin Bergram, Stergios Kechagias, Patrik Nasr, Mattias Ekstedt (2024) Activin A levels in metabolic dysfunction-associated steatotic liver disease associates with fibrosis and the PNPLA3 I148M variant Scandinavian Journal of Gastroenterology (Article in journal) Continue to DOI

2023

William Lövfors, Rasmus Magnusson, Cecilia Jönsson, Mika Gustafsson, Charlotta S. Olofsson, Gunnar Cedersund, Elin Nyman (2023) A comprehensive mechanistic model of adipocyte signaling with layers of confidence npj Systems Biology and Applications, Vol. 9, Article 24 (Article in journal) Continue to DOI
Patrik Nasr, Mikael Forsgren, Wile Balkhed, Cecilia Jönsson, Nils Dahlström, Christian Simonsson, Shan Cai, Anna Cederborg, Martin Henriksson, Henrik Stjernman, Martin Rejler, Daniel Sjoegren, Gunnar Cedersund, Wolf Bartholomä, Ingvar Rydén, Peter Lundberg, Stergios Kechagias, Olof Dahlqvist Leinhard, Mattias Ekstedt (2023) A rapid, non-invasive, clinical surveillance for CachExia, sarcopenia, portal hypertension, and hepatocellular carcinoma in end-stage liver disease: the ACCESS-ESLD study protocol BMC Gastroenterology, Vol. 23, Article 454 (Article in journal) Continue to DOI

2022

William Lövfors, Cecilia Jönsson, Charlotta S. Olofsson, Elin Nyman, Gunnar Cedersund (2022) A comprehensive mechanistic model of adipocyte signaling with layers of confidence

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