AG Systems Biology and Translation in Psychiatry

Advances in high throughput techniques have led to an increasing wealth of biological data, facilitating the description of human biology in health and disease in unprecedented detail. At the same time, the wealth of Omics data challenges our ways to treat, analyse, interpret, and translate data and insights into the clinical setting. The AG Systems Biology and Translation in Psychiatry works on new overarching analysis paradigms integrating deterministic modelling approaches with population statistics for translating insights from Omics data into clinical practice in the realm of neuropsychiatric diseases. Currently, we are working on three levels of systems biological modelling.

Microbiome Modelling

The microbiome contributes crucially to human health and disease through its metabolic activity. We develop theoretical concepts allowing for the population statistics treatment of constraint-based community models and apply them to clinical data, enabling the functional characterisation of the microbiome in detail. Statistical analyses of personalised constraint-based community models additionally allow for novel insights into microbial ecology.

Integrative Whole Body Modelling

Based on the Virtual Metabolic Human database, Thiele et al. (2020) developed comprehensive organ-resolved, sex-specific whole body models, translating over 80000 metabolic reactions, human physiology, and the human microbiome into one mathematical formalism that enables personalised interrogation of metabolic phenotypes. It was successful in predicting known inborn errors of human metabolism and novel gene-metabolite associations discovered by genome wide association studies. We now explore the utility of whole body models for understanding the metabolic component of neurodegenerative diseases such as Alzheimer’s disease or Parkinson’s disease.

Cohort-based metabolic modelling of stress-related disorders and brain-aging

We develop methods to facilitate in silico modelling of large cohort data, aiming to generate virtual twins of the Study of Health in Pomerania and patient cohorts from the GANI_MED project via the utilisation of personalised whole body models. Model parameters can then be utilized for predicting health outcomes and investigation of risk factors and etiological conditions for stress-related disorders and processes of brain-aging.

Ongoing collaborations of the AG Systems Biology and Translation in Psychiatry

• Molecular Systems Physiology Group, National University of Ireland, Galway, Ireland
• Institute of Genetic Epidemiology, University Medicine Freiburg, Freiburg, Germany
• Immune Endocrine and Epigenetics Group, Luxembourg Institute of Health, Esch-sur-Alzette, Luxembourg
• German Center for Neurodegenerative Diseases, Partner Site Greifswald/Rostock, Rostock, Germany
• Cohen Lab on Aging, Systems, and Statistics, University of Sherbrooke, Sherbrooke, Canada

Third party projects

Modelling the influence of the gut microbiome on patterns of brain aging in the general population by combining constraint-based modelling approaches with large cohort magnetic resonance imaging data (DFG - https://gepris.dfg.de/gepris/projekt/497582271?language=en)

Towards a universal characterization of human aging 1: Immunosenescence. (Impetus Grants)

Key Publications

Baldini, F., Hertel, J., Sandt, E., Thinnes, C.C., Neuberger-Castillo, L., Pavelka, L., Betsou, F., Krüger, R., Thiele, I., 2020. Parkinson’s disease-associated alterations of the gut microbiome predict disease-relevant changes in metabolic functions. BMC biology 18, 1–21.

Charalambous, E.G., Mériaux, S.B., Guebels, P., Muller, C.P., Leenen, F.A.D., Elwenspoek, M.M.C., Thiele, I., Hertel, J., Turner, J.D., 2021. Early-Life Adversity Leaves Its Imprint on the Oral Microbiome for More Than 20 Years and Is Associated with Long-Term Immune Changes. Int J Mol Sci 22, 12682. https://doi.org/10.3390/ijms222312682

Cheng, Y., Schlosser, P., Hertel, J., Sekula, P., Oefner, P.J., Spiekerkoetter, U., Mielke, J., Freitag, D.F., Schmidts, M., Kronenberg, F., 2021. Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism. Nature communications 12, 1–15.

Heinken, A., Basile, A., Hertel, J., Thinnes, C., Thiele, I., 2021a. Genome-Scale Metabolic Modeling of the Human Microbiome in the Era of Personalized Medicine. Annu Rev Microbiol 75, 199–222. https://doi.org/10.1146/annurev-micro-060221-012134

Heinken, A., Hertel, J., Thiele, I., 2021b. Metabolic modelling reveals broad changes in gut microbial metabolism in inflammatory bowel disease patients with dysbiosis. npj Syst Biol Appl 7, 19. https://doi.org/10.1038/s41540-021-00178-6

Hertel, J., Fässler, D., Heinken, A., Weiß, F.U., Rühlemann, M., Bang, C., Franke, A., Budde, K., Henning, A.-K., Petersmann, A., Völker, U., Völzke, H., Thiele, I., Grabe, H.-J., Lerch, M.M., Nauck, M., Friedrich, N., Frost, F., 2022. NMR Metabolomics Reveal Urine Markers of Microbiome Diversity and Identify Benzoate Metabolism as a Mediator between High Microbial Alpha Diversity and Metabolic Health. Metabolites 12, 308. https://doi.org/10.3390/metabo12040308

Hertel, J., Frenzel, S., Koenig, J., Wittfeld, K., Fuellen, G., Holtfreter, B., Pietzner, M., Friedrich, N., Nauck, M., Voelzke, H., 2019a. The informative error: a framework for the construction of individualized phenotypes. Statistical methods in medical research 28, 1427–1438.

Hertel, J., Friedrich, N., Wittfeld, K., Pietzner, M., Budde, K., Van der Auwera, S., Lohmann, T., Teumer, A., Völzke, H., Nauck, M., 2016. Measuring biological age via metabonomics: the metabolic age score. Journal of proteome research 15, 400–410.

Hertel, J., Harms, A.C., Heinken, A., Baldini, F., Thinnes, C.C., Glaab, E., Vasco, D.A., Pietzner, M., Stewart, I.D., Wareham, N.J., 2019b. Integrated analyses of microbiome and longitudinal metabolome data reveal microbial-host interactions on sulfur metabolism in Parkinson’s disease. Cell reports 29, 1767-1777. e8.

Hertel, J., Heinken, A., Martinelli, F., Thiele, I., 2021. Integration of constraint-based modeling with fecal metabolomics reveals large deleterious effects of Fusobacterium spp. on community butyrate production. Gut Microbes 13, 1915673. https://doi.org/10.1080/19490976.2021.1915673

Thiele, I., Sahoo, S., Heinken, A., Hertel, J., Heirendt, L., Aurich, M.K., Fleming, R.M., 2020. Personalized whole‐body models integrate metabolism, physiology, and the gut microbiome. Molecular systems biology 16, e8982.

*Authors contributed equally

Full publication list: Google scholar

Prof. Dr. Johannes Hertel

johannes.hertel@med.uni-greifswald.de

Tel.: +49 (0) 3834 86 6907

Professor (W1) in Systems Biology and Translation in Psychiatry

Department of Psychiatry and Psychotherapy

University Medicine Greifswald

Ellernholzstr. 1-2

17489 Greifswald, Germany