Our research uses genetic and genomic tools to identify the neurobiological mechanisms contributing to risk for psychiatric disorders, including autism and schizophrenia. Furhter, we strive to translate biological insights from basic research into the clinic.
We use microarray and RNA-sequencing technologies to characterize altered patterns of gene expression in post-mortem human brain samples from subjects with major neuropsychiatric disorders, including autism, schizophrenia, bipolar disorder, depression, and alcoholism as well as matched controls. Emerging data indicate a substantial overlap in genetic risk shared across multiple distinct psychiatric disorders. Yet, this finding remains largely unexplained, in part due to a lack of systematic, comparable phenotyping across disorders. We use the transcriptome as a quantitative measurement close to the underlying genetic risk for disease. Our work has identified a clear pattern of shared and distinct gene-expression perturbations across these conditions, including shared neuronal and synaptic pathways downregulated in ASD, schizophrenia, and bipolar disorder, activation of astrocytes in ASD and schizophrenia, and prominent upregulation of microglia in ASD alone. The degree of sharing of transcriptional dysregulation was strongly related to polygenic overlap across disorders, indicating a significant genetic component. This work provides a systems-level view of the neurobiological architecture of major neuropsychiatric illness and demonstrate pathways of molecular convergence and specificity to inform potential future mechanistic-based interventions.
Funded by the Simons Foundation for Autism Research (SFARI), we are working to develop an integrative model for the relationship between polygenic risk for ASD, cellular and molecular changes in microglial and astrocyte populations in ASD brain. We will use measures of RNAseq, DNA methylation, and genotyping data from post-mortem human brain samples across 10 cortical regions from subjects with ASD and matched controls to map in detail the temporal and spatial trajectories of microglial and astrocyte activation in ASD. Epigenetic markers will be used to provide a readout of the neuron-to-glia proportion within each sample. Gene co-expression networks will be employed to identify specific cellular and molecular pathways disrupted in ASD cortex, which will be integrated with polygenic risk scores to identify potential causal genetic drivers.
Autism spectrum disorders (ASD) are highly disabling, persistent neurodevelopmental disorders. There are no available treatments for core symptoms of ASD. Consequently, there is tremendous need to develop novel, biologically-informed interventions. Emerging evidence implicates hyperactivity of microglia, the resident immune cells of the central nervous system (CNS), in ASD pathophysiology. However, the functional consequences of microglial activation, and its promise as a therapeutic target, remain unknown. This study seeks to directly interrogate in vivo microglial activation in ASD using positron emission tomography (PET) imaging with the radiotracer N-(2,5-dimethoxy-benzyl)-N-(5-fluoro-2-phenoxyphenyl)-acetamide, labeled with carbon-11 ([11C]-DAA1106), which binds to the mitochondrial translocator protein (TSPO), a marker of neuroinflammation and activated microglia.
Adults with ASD and healthy controls will be assessed for baseline differences in [11C]-DAA1106 binding. ASD subjects will then undergo 12 weeks of open-label treatment with minocycline, an FDA-approved antibiotic that blocks microglial activation. [11C]-DAA1106 binding will be measured post-treatment to confirm target engagement, as compared with test-retest assessment in controls. ASD subjects will be evaluated pre- and post-treatment for several secondary and exploratory measures, including clinical global impression (CGI), measures of anxiety and emotion regulation, as well as cognitive control and processing speed. Peripheral cytokine levels will be assessed for correlation with microglial activation.
For further information, see https://clinicaltrials.gov/ct2/show/NCT03117530
Integrative neurobiological framework for interpretation of disease-associated genetic variation (Gandal et al., 2016)