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Biology subjects

Mitchell, A. A.

Publications and source records attributed to Mitchell, A. A..

2 recordsLinked to original sources

Chronic Stress Alters Dorsal Bed Nucleus of Stria Terminalis Synaptic Neurotransmission in a Dravet Syndrome Mouse Model

Stress is a commonly reported seizure precipitant and may contribute to the development of psychiatric comorbidities in epilepsy, yet how chronic stress interacts with epileptic circuits remains poorly understood. We investigated the impact of chronic restraint stress on physiological, behavioral, and synaptic outcomes in a mouse model of Dravet syndrome, specifically corticotropin-releasing factor (CRF) neurons in the bed nucleus of the stria terminalis (BNST), a stress-responsive region implicated in epilepsy patients. Chronic restraint stress produced divergent hypothalamic-pituitary-adrenal axis responses, with stressed Dravet syndrome mice exhibiting elevated corticosterone, increased mortality in females, and increased locomotion and anxiety-like behavior. Ex vivo electrophysiological recordings revealed that chronic stress increased spontaneous excitatory event frequency onto BNST CRF neurons in both genotypes and selectively increased sEPSC and sIPSC amplitude in Dravet syndrome mice. Evoked recordings demonstrated genotype-specific effects of stress on glutamatergic transmission in CRF neurons of the DS group. This suggests greater stress-dependent remodeling of spontaneous and evoked synaptic activity in DS. These findings suggest chronic stress may worsen physiological and behavioral outcomes in Dravet syndrome and promote specific maladaptive alterations in BNST CRF circuitry. More broadly, these results suggest that stress interacts with seizure vulnerability and potentially contributes to neuropsychiatric comorbidities and epilepsy.

neuroscience↗

Phenotype-specific information improves prediction of functional impact for noncoding variants

Functional characterization of the noncoding genome is essential for the biological understanding of gene regulation and disease. Here, we introduce the computational framework PINES (Phenotype-Informed Noncoding Element Scoring) which predicts the functional impact of noncoding variants by integrating epigenetic annotations in a phenotype-dependent manner. A unique feature of PINES is that analyses may be customized towards genomic annotations from cell types of the highest relevance given the phenotype of interest. We illustrate that PINES identifies functional noncoding variation more accurately than methods that do not use phenotype-weighted knowledge, while at the same time being flexible and easy to use via a dedicated web portal.

genomics↗