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Abstract Traumatic Brain Injury (TBI) induces neuroinflammatory response that can initiate epileptogenesis, which develops into epilepsy. Recently, we identified anti-convulsive effects of naltrexone, a mu-opioid receptor (MOR) antagonist, used to treat drug addiction. While blocking opioid receptors can reduce inflammation, it is unclear if post-TBI seizures can be prevented by blocking MORs. Here, we tested if naltrexone prevents neuroinflammation and/or seizures post-TBI. TBI was induced by a modified Marmarou Weight-Drop (WD) method on 4-week-old C57BL/6J male mice. Mice were placed in two groups: non-telemetry assessing the acute effects or in telemetry monitoring for interictal events and spontaneous seizures both following TBI and naltrexone. Molecular, histological and neuroimaging techniques were used to evaluate neuroinflammation, neurodegeneration and fiber track integrity at 8 days and 3 months post-TBI. Peripheral immune responses were assessed through serum chemokine/cytokine measurements. Our results show an increase in MOR expression, nitro-oxidative stress, mRNA expression of inflammatory cytokines, microgliosis, neurodegeneration, and white matter damage in the neocortex of TBI mice. Video-EEG revealed increased interictal events in TBI mice, with 71% mice developing post-traumatic seizures (PTS). Naltrexone treatment ameliorated neuroinflammation, neurodegeneration, reduced interictal events and prevented seizures in all TBI mice, which makes naltrexone a promising candidate against PTS, TBI-associated neuroinflammation and epileptogenesis in a WD model of TBI.

Abstract Accumulating evidence suggests that type I interferon (IFN-I) signaling is a key contributor to immune cell-mediated neuropathology in neurodegenerative diseases. Recently, we demonstrated a robust upregulation of type I interferon-stimulated genes in microglia and astrocytes following experimental traumatic brain injury (TBI). The specific molecular and cellular mechanisms by which IFN-I signaling impacts the neuroimmune response and neuropathology following TBI remains unknown. Using the lateral fluid percussion injury model (FPI) in adult male mice, we demonstrated that IFN α/β receptor (IFNAR) deficiency resulted in selective and sustained blockade of type I interferon-stimulated genes following TBI as well as decreased microgliosis and monocyte infiltration. Molecular alteration of reactive microglia also occurred with diminished expression of genes needed for MHC class I antigen processing and presentation following TBI. This was associated with decreased accumulation of cytotoxic T cells in the brain. The IFNAR-dependent modulation of the neuroimmune response was accompanied by protection from secondary neuronal death, white matter disruption, and neurobehavioral dysfunction. These data support further efforts to leverage the IFN-I pathway for novel, targeted therapy of TBI.

Genes associated with risk for brain disease exhibit characteristic expression patterns that reflect both anatomical and cell type relationships. Brain-wide transcriptomic patterns of disease risk genes provide a molecular-based signature, based on differential co-expression, that is often unique to that disease. Brain diseases can be compared and aggregated based on the similarity of their signatures which often associates diseases from diverse phenotypic classes. Analysis of 40 common human brain diseases identifies 5 major transcriptional patterns, representing tumor-related, neurodegenerative, psychiatric and substance abuse, and 2 mixed groups of diseases affecting basal ganglia and hypothalamus. Further, for diseases with enriched expression in cortex, single-nucleus data in the middle temporal gyrus (MTG) exhibits a cell type expression gradient separating neurodegenerative, psychiatric, and substance abuse diseases, with unique excitatory cell type expression differentiating psychiatric diseases. Through mapping of homologous cell types between mouse and human, most disease risk genes are found to act in common cell types, while having species-specific expression in those types and preserving similar phenotypic classification within species. These results describe structural and cellular transcriptomic relationships of disease risk genes in the adult brain and provide a molecular-based strategy for classifying and comparing diseases, potentially identifying novel disease relationships.

Copy number variations (CNV) involving multiple genes are ideal models to study polygenic neuropsychiatric disorders. Since 22q11.2 deletion is regarded as the most important single genetic risk factor for developing schizophrenia, characterizing the effects of this CNV on neural networks offers a unique avenue towards delineating polygenic interactions conferring risk for the disorder. We used a Df(h22q11)/+ mouse model of human 22q11.2 deletion to dissect gene expression patterns that would spatially overlap with differential resting-state functional connectivity (FC) patterns in this model (N = 12 Df(h22q11)/+ mice, N = 10 littermate controls). To confirm the translational relevance of our findings, we analyzed tissue samples from schizophrenia patients and healthy controls using machine learning to explore whether identified genes were co-expressed in humans. Additionally, we employed the STRING protein-protein interaction database to identify potential interactions between genes spatially associated with hypo- or hyper-FC. We found significant associations between differential resting-state connectivity and spatial gene expression patterns for both hypo- and hyper-FC. Two genes, Comt and Trmt2a, were consistently over-expressed across all networks. An analysis of human datasets pointed to a disrupted co-expression of these two genes in the brain in schizophrenia patients, but not in healthy controls. Our findings suggest that COMT and TRMT2A form a core genetic component implicated in differential resting-state connectivity patterns in the 22q11.2 deletion. A disruption of their co-expression in schizophrenia patients points out a prospective cause for the aberrance of brain networks communication in 22q11.2 deletion syndrome on a molecular level.