Computational modelling of brain energy metabolism in schizophrenia : insights from post-mortem RNA sequencing data
Mäkinen, Ilona (2024)
2024
Bioteknologian ja biolääketieteen tekniikan maisteriohjelma - Master's Programme in Biotechnology and Biomedical Engineering
Lääketieteen ja terveysteknologian tiedekunta - Faculty of Medicine and Health Technology
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Hyväksymispäivämäärä
2024-11-28
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202411069968
https://urn.fi/URN:NBN:fi:tuni-202411069968
Tiivistelmä
Schizophrenia is a complex psychiatric disorder characterized by a wide variety of symptoms such as hallucinations, delusions, lack of pleasure and problems in attention and memory. The aetiology of the disorder is not fully understood, but dysregulation of multiple neurotransmitter systems as well as genetic and environmental factors contribute to the pathology of schizophrenia. Furthermore, schizophrenia is associated with disruptions in brain energy metabolism. Since signalling activities of the brain require constant energy supply, problems in brain energy metabolism can cause abnormalities in the synaptic activity.
This thesis investigated the connection between the expression of cytosolic energy metabolism genes and the concentrations of central energy metabolites in neurons and astrocytes. First, a differential expression (DE) analysis was performed to study the gene expression differences between schizophrenia patients and healthy individuals. The DE analysis utilized post-mortem bulk RNA sequencing data from the prefrontal cortex (PFC) and the anterior cingulate cortex (ACC). After the DE analysis, cell-type specific gene expressions were imputed for neurons and astrocytes. Finally, a computational model of brain energy metabolism was used to simulate the gene expression changes. The aim of the simulations was to study how the gene expression alterations influence the concentrations of key metabolites.
The DE analysis revealed that there are 23 differentially expressed cytosolic energy metabolism genes in the PFC and nine in the ACC. Moreover, around two thirds of the DE genes were downregulated in schizophrenia in both brain areas. Yet, the simulation results showed that altered expression of most of the genes had no significant effects on concentrations of the considered metabolites. However, decreased expression of neuronal PFKM gene in the PFC caused significant changes in all but one metabolite in both neurons and astrocytes. In addition, decreased neuronal LDHB in the ACC caused smaller yet significant changes in a few neuronal metabolites.
In conclusion, the results obtained in this thesis provide additional support for brain energy metabolism dysregulation in schizophrenia. The DE analysis results suggest that dysregulation of brain energy metabolism is region-specific, and that the dysregulation is stronger in the PFC than the ACC. Lastly, the simulation results show that most cytosolic energy metabolism genes do not alone have the power to significantly alter the concentrations of central energy metabolites. This suggests that these gene expression alterations do not alone cause the complex disruptions of energy metabolism in schizophrenia.
This thesis investigated the connection between the expression of cytosolic energy metabolism genes and the concentrations of central energy metabolites in neurons and astrocytes. First, a differential expression (DE) analysis was performed to study the gene expression differences between schizophrenia patients and healthy individuals. The DE analysis utilized post-mortem bulk RNA sequencing data from the prefrontal cortex (PFC) and the anterior cingulate cortex (ACC). After the DE analysis, cell-type specific gene expressions were imputed for neurons and astrocytes. Finally, a computational model of brain energy metabolism was used to simulate the gene expression changes. The aim of the simulations was to study how the gene expression alterations influence the concentrations of key metabolites.
The DE analysis revealed that there are 23 differentially expressed cytosolic energy metabolism genes in the PFC and nine in the ACC. Moreover, around two thirds of the DE genes were downregulated in schizophrenia in both brain areas. Yet, the simulation results showed that altered expression of most of the genes had no significant effects on concentrations of the considered metabolites. However, decreased expression of neuronal PFKM gene in the PFC caused significant changes in all but one metabolite in both neurons and astrocytes. In addition, decreased neuronal LDHB in the ACC caused smaller yet significant changes in a few neuronal metabolites.
In conclusion, the results obtained in this thesis provide additional support for brain energy metabolism dysregulation in schizophrenia. The DE analysis results suggest that dysregulation of brain energy metabolism is region-specific, and that the dysregulation is stronger in the PFC than the ACC. Lastly, the simulation results show that most cytosolic energy metabolism genes do not alone have the power to significantly alter the concentrations of central energy metabolites. This suggests that these gene expression alterations do not alone cause the complex disruptions of energy metabolism in schizophrenia.