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Research group

Systems medicine

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Matej Oresic

Research subject

Our main research areas include exposomics and metabolomics applications in biomedical research and systems medicine. We are particularly interested in how external (e.g., environmental chemicals) and internal (e.g., gut microbiome, metabolome, immune system status) exposures impact life-course health and mediate the risk of various diseases. Such in depth understanding of health and disease is crucial if one is to implement precision medicine and to understand how various environmental factors shape human health. The primary medical research areas have been non-alcoholic fatty liver disease, metabolic co-morbidities in psychotic disorders, traumatic brain injury, and investigations of metabolic disturbances preceding immune mediated inflammatory disorders such as type 1 diabetes and celiac disease. We are also interested in the development of novel computational tools for the studis of exposome and metabolome.

The menbers of Systems Medicine

Our research has been funded by several national and international funding agencies, including European Union, Swedish Research Council, National Institutes of Health (USA), Wellcome Trust (UK), Academy of Finland, Juvenile Diabetes Research Foundation (USA) and Human Frontiers Science Program. The PI of the Systems Medicine team, Professor Matej Orešič, currently coordinates new Horizon Europe project "Inflammation in human early life: targeting impacts on life-course health – INITIALISE"

Here are four project examples from our research:

Early metabolic disturbances and exposome in progression to type 1 diabetes and other immune-mediated diseases
Studies by us and others suggest highlight complex crosstalk between environment, gut microbiome, metabolic and immune system factors in early progression to T1D. Our recent representative publications on the topic:

1. A. McGlinchey, et al. Prenatal exposure to perfluoroalkyl substances modulates neonatal serum phospholipids, increasing risk of type 1 diabetes. Environ Int 143, 105935 (2020).
2. P. Sen, et al. Quantitative genome-scale metabolic modeling of human CD4+ T-cell differentiation reveals subset-specific regulation of glycosphingolipid pathways. Cell Rep 37(6), 109973 (2021).
3. S. Lamichhane, et al. Dysregulation of secondary bile acid metabolism precedes islet autoimmunity and type 1 diabetes. Cell Rep Med, 100762 (2022).

Metabolomics data processing
Typical metabolomic experiments can produce large amounts of data. Processing of such complex datasets is a crucial step that has big impact on extent and quality at which the metabolite identification and quantification can be made, and thus on the ultimate biological interpretation of the results. Prof. Orešič initiated the MZmine project, which is one of the most popular open-source software packages for mass spectrometry-based metabolomics data processing. Representative publications:

1. T. Pluskal, et al. MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data, BMC Bioinformatics 11, 395 (2010).
2. R. Schmid et al. Integrative analysis of multimodal mass spectrometry data in MZmine 3. Nat Biotechnol (2023).

Metabolome and exposome in obesity and its metabolic co-morbidities
Due to its tight homeostatic control, it is not surprising that altered lipid metabolism has a global reach as a pathogenic mechanism. In fact, the alterations in lipid biology may be an important underlying pathogenic link among some of these disorders, which could explain the co-morbidities related to obesity and metabolic syndrome.

1. H. K. Pedersen, et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 535, 376-381 (2016).
2. T. Hyötyläinen, et al. Genome-scale study reveals reduced metabolic adaptability in patients with non-alcoholic fatty liver disease. Nat Commun 7, 8994 (2016).
3. M. Masoodi, et al. Metabolomics and lipidomics in NAFLD: biomarkers and non- invasive diagnostic tests. Nat Rev Gastroenterol Hepatol 18(12), 835-856 (2021).
4. P. Sen, et al. Exposure to environmental contaminants is associated with altered hepatic lipid metabolism in non-alcoholic fatty liver disease. J Hepatol 76(2), 283-293 (2022).
5. J. McGlinchey, et al. Metabolic signatures across the full spectrum of nonalcoholic fatty liver disease. JHEP Rep 4(5), 100477 (2022).

Biomarkers of CNS- and psychiatric disorders
Concentration changes of specific groups of metabolites may be sensitive to pathogenically relevant factors such as genetic variation, diet, age, immune system status or gut microbiota, and their study may therefore be a powerful tool for characterization of complex phenotypes affected by both genetic and environmental factors. There is a clear opportunity to use metabolomics in the identification of novel circulating biomarkers of disorders of the central nervous system.

1. S. Lamichhane, et al. Association Between Circulating Lipids and Future Weight Gain in Individuals With an At-Risk Mental State and in First-Episode Psychosis. Schizophr Bull 47 (1), 160-169 (2021).
2. M. Dickens, et al. Dysregulated lipid metabolism precedes onset of psychosis. Biol Psych 89 (3), 288-297 (2021).
3. I. Thomas, et al. Serum metabolome associated with severity of acute traumatic brain injury. Nat Commun 13(1), 2545 (2022).

Research funding bodies

  • Academy of Finland
  • EU Horizon 2020
  • EU Horizon Europe
  • The Knowledge Foundation
  • National Institute of Health (NIH)
  • Novo Nordisk Foundation
  • The European Cooperation in Science and Technology (COST)
  • Swedish Research Council