Periodic Reporting for period 4 - SCHIZTYPE (Brain cell type-specific interactions and schizophrenia)
Okres sprawozdawczy: 2023-10-01 do 2024-03-31
Schizophrenia is a debilitating disorder affecting millions of people worldwide with a huge cost to those affected, those who care for them, and to society. Schizophrenia is largely a genetic disorder in that the risk of getting schizophrenia is to a large extent determined by the DNA code that we inherit from our parents. Thanks to recent advances in human genetic studies we now know more about the complex architecture of the genetic risk where a vast majority of schizophrenia cases are due to a combination of hundreds of risk mutations, each contributing a small amount of risk. How schizophrenia arises in the brain of patients is however not understood. Schizophrenia is complex also in terms of cellular pathology in patients with most cell types having been implicated at some point. However, our previous analysis has indicated that all the risk genes, based on where these genes are used, point towards a limited set of brain cells being central to the risk for schizophrenia. Cortical excitatory (increases activity) cell classes have the highest signal while less signal was observed in oligodendrocytes (support cells) and inhibitory (decrease activity) neurons. In SCHIZTYPE we first aim to identify the convergent (overlapping) genetic programs inside cortical excitatory neurons that are changed in disease using mouse models and human postmortem brains. We also want to understand which cell types and brain regions are crucial for the disorder and understand how genetic risk in patients are distributed across these different modalities. Lack of understanding of the disease biology of schizophrenia is one major reason the larger pharmaceutical industry mentions are divesting in the disease area. Thus, it is of crucial interest for society to invest in efforts towards explicating the mechanisms behind the disorder as this has the chance to many fold societal investment in schizophrenia research. The overall goal of the project was to identify convergent molecular and cellular pathways downstream of different genetic risk models.
In this project, we have used advanced molecular techniques to measure the expression (usage) of different genes across different brain cell types (scRNAseq) in an area called the prefrontal cortex. This area has been for many years shown to be crucial for many symptoms of schizophrenia. A majority of risk genes for schizophrenia are involved in the function of synapses (connections between neurons) and accordingly, when we analyze which brain cell types that express the risk genes we see that neurons are enriched. We have also shown that different cell types have largely non-overlapping sets of risk genes. One outstanding question was how different neurons might be implicated in schizophrenia although the signal comes from synaptic genes that were often thought to be common. In one study from this project, we resolve this by showing that a large majority of synaptic genes are specifically expressed and that there are three different modes of variability (class-dependent, cell type-dependent, and continuous variation). Along these lines, we have within SCHIZTYPE also refined the risk-enrichment analysis (which showed that specific types of neurons were enriched) by analyzing recent large-scale human data sets of the entire brain. This has allowed us to perform a data-driven prioritization for cell types and brain regions which are important for the development of the disorder. This analysis confirmed previous findings of neuronal enrichment but also indicated that, in addition to the prefrontal cortex, the amygdala and hippocampus are important for schizophrenia etiology. The findings from this unbiased approach were confirmed using two independent datasets and methods in a deep neural network analysis of functional Magnetic Resonance Imaging (fMRI) of patients.
To identify convergent risk in excitatory cells in the prefrontal cortex we have performed scRNAseq of cells from five different mouse models of schizophrenia risk. Each mouse model carries a mutation (copy number variation; CNV) that in humans greatly increases the risk for schizophrenia. When comparing the changes in gene expression between the different mouse lines we observed no significant overlap. This is very much in contrast to our hypothesis and we conclude that for large-effect size schizophrenia risk variants the behavioural convergence observed in patients cannot be observed on a molecular level. This suggests that perhaps convergence rather happens on a network level.
Within SCHIZTYPE we have also performed a large-scale scRNAseq study of postmortem prefrontal cortex tissue from patients who suffered from schizophrenia. First, we observed near-to-no overlap with the findings from the five mouse models. On the other hand, we show that neurons and astrocytes (a type of support cell) have perturbations in the energy supply (mitochondrial function) and that the genetic programs that are involved also contain synaptic genes. Importantly, these genetic programs are also enriched for schizophrenia risk arguing that these changes are casual, rather than due to the disease. We have also performed transcriptomic analysis on tissue sections from human postmortem material which confirmed the findings from the scRNAseq study that we do not see any gross changes in the number of different cell types or their location in patients.
We are currently perturbing the expression of important genes that we have identified in mouse and human samples analyzed to test the role of those genes in the cell and how perturbation of one cell can affect the function of neighboring cells.
To identify convergent risk in excitatory cells in the prefrontal cortex we have performed scRNAseq of cells from five different mouse models of schizophrenia risk. Each mouse model carries a mutation (copy number variation; CNV) that in humans greatly increases the risk for schizophrenia. When comparing the changes in gene expression between the different mouse lines we observed no significant overlap. This is very much in contrast to our hypothesis and we conclude that for large-effect size schizophrenia risk variants the behavioural convergence observed in patients cannot be observed on a molecular level. This suggests that perhaps convergence rather happens on a network level.
Within SCHIZTYPE we have also performed a large-scale scRNAseq study of postmortem prefrontal cortex tissue from patients who suffered from schizophrenia. First, we observed near-to-no overlap with the findings from the five mouse models. On the other hand, we show that neurons and astrocytes (a type of support cell) have perturbations in the energy supply (mitochondrial function) and that the genetic programs that are involved also contain synaptic genes. Importantly, these genetic programs are also enriched for schizophrenia risk arguing that these changes are casual, rather than due to the disease. We have also performed transcriptomic analysis on tissue sections from human postmortem material which confirmed the findings from the scRNAseq study that we do not see any gross changes in the number of different cell types or their location in patients.
We are currently perturbing the expression of important genes that we have identified in mouse and human samples analyzed to test the role of those genes in the cell and how perturbation of one cell can affect the function of neighboring cells.
We have made important findings that will cause the field to re-evaluate the current state-of-the-art. The finding that we, in a well-powered study, across different mouse models for schizophrenia risk and patient material could not identify molecular convergence will have important implications. First, it highlights that these CNV models are not models of the disorder schizophrenia but of the risk. Also, it suggests that the convergence observed in behavior lies at another level of biology than at gene-regulatory programs. Our study has important caveats so further research is necessary to confirm our findings. Another observation from these studies is that we cannot detect any gross changes in cell numbers, cell type composition, or the relative position of cell types arguing that these important processes that occur during embryonic development are likely not affected in patients. This is in contrast to several observations in studies of stem cells derived from patients arguing an important limitation in the interpretation of such studies. Furthermore, our studies reveal an important, potentially causal role for mitochondrial function in schizophrenia. We also show, for the first time with two different completely data-driven independent approaches that the brain regions of the amygdala and the hippocampus are crucial for schizophrenia etiology. Ultimately, we have in SCHIZTYPE improved our understanding of schizophrenia biology and its origin significantly.