Computational modelling of brain energy metabolism in schizophrenia

Tutkimustuotos: AbstraktiTieteellinen

Abstrakti

Introduction
Schizophrenia is a psychiatric disorder which affects 0.45 % of adults globally [1]. The cause of schizophrenia is not fully understood, but dysregulation of multiple neurotransmitters, environmental factors and genetics contribute to the disorder [2]. Schizophrenia has also been linked to abnormalities in brain energy metabolism which can cause dysregulation of synaptic activity [2]. That is why the aim of this thesis was to examine how the expression of cytosolic energy metabolism genes affects the concentrations of key energy metabolites in neurons and astrocytes in response to neuronal stimulus.

Methods
Differential expression (DE) analysis was used to study the gene expression differences between schizophrenia patients (SCZ) and healthy controls (HC). The DE analysis used post-mortem bulk RNA sequencing data from the prefrontal cortex (PFC) and the anterior cingulate cortex (ACC) of the human brain [3]. Cell-type specific gene expressions were estimated by using CIBERSORTx tool. A computational model of brain energy metabolism [4] and Copasi simulator were used to simulate the gene expression changes and study their effect on central energy metabolite concentrations.

Results
The DE analysis showed that there are 3 upregulated genes in the ACC and 7 in the PFC, and 6 downregulated genes in the ACC and 16 in the PFC in SCZ vs HC. The single-gene simulations showed that altered expression of most genes did not cause significant changes in any metabolite concentrations. However, decreased expression of neuronal genes PFKM in the PFC and LDHB in the ACC caused some significant alterations in the metabolite concentrations (Figure 1).

Discussion
The DE analysis revealed region-specific bioenergetic dysregulation and supports the hypofrontality hypothesis of lower energy metabolism in frontal areas of the schizophrenic brain [5]. The simulation results showed that downregulation of two important glycolysis enzymes caused significant alterations in bioenergetics in SCZ. The use of these novel methods increases our knowledge of bioenergetics of schizophrenia and can provide new targets for personalized treatment.

References
1. Institute of health Metrics and Evaluation (IHME). Global Health Data Exchange (GHDx). http://ghdx.healthdata.org/gbd-results-tool?params=gbd-api-2019-permalink/27a7644e8ad28e739382d31e77589dd7 (Accessed 3.2.2025).
2. C. R. Sullivan et al., Biol Psychiatry, 83:739-750, 2018.
3. G. E. Hoffman et al., Sci Data, 6:180, 2019.
4. F. Winter et al., J Cereb Blood Flow Metab, 8:304-316, 2018.
5. L. Townsend et al., Psychol Med, 53:4880-4897, 2023.
AlkuperäiskieliEnglanti
TilaJulkaistu - 13 helmik. 2025
OKM-julkaisutyyppiEi OKM-tyyppiä
TapahtumaLääketieteellisen fysiikan ja tekniikan (LFT) päivät - Tampere University, Finland, Tampere, Suomi
Kesto: 13 helmik. 202514 helmik. 2025

Conference

ConferenceLääketieteellisen fysiikan ja tekniikan (LFT) päivät
Maa/AlueSuomi
KaupunkiTampere
Ajanjakso13/02/2514/02/25

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