Corticohippocampal circuit dysfunction in a mouse model of Dravet syndrome

Dravet syndrome (DS) is a neurodevelopmental disorder due to pathogenic variants in SCN1A encoding the Nav1.1 sodium channel subunit, characterized by treatment-resistant epilepsy, temperature-sensitive seizures, developmental delay/intellectual disability with features of autism spectrum disorder, and increased risk of sudden death. Convergent data suggest hippocampal dentate gyrus (DG) pathology in DS (Scn1a+/-) mice. We performed two-photon calcium imaging in brain slice to uncover a profound dysfunction of filtering of perforant path input by DG in young adult Scn1a+/- mice. This was not due to dysfunction of DG parvalbumin inhibitory interneurons (PV-INs), which were only mildly impaired at this timepoint; however, we identified enhanced excitatory input to granule cells, suggesting that circuit dysfunction is due to excessive excitation rather than impaired inhibition. We confirmed that both optogenetic stimulation of entorhinal cortex and selective chemogenetic inhibition of DG PV-INs lowered seizure threshold in vivo in young adult Scn1a+/- mice. Optogenetic activation of PV-INs, on the other hand, normalized evoked responses in granule cells in vitro. These results establish the corticohippocampal circuit as a key locus of pathology in Scn1a+/- mice and suggest that PV-INs retain powerful inhibitory function and may be harnessed as a potential therapeutic approach toward seizure modulation.


Sample-size estimation
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Replicates
• You should report how often each experiment was performed • You should include a definition of biological versus technical replication • The data obtained should be provided and sufficient information should be provided to indicate the number of independent biological and/or technical replicates • If you encountered any outliers, you should describe how these were handled • Criteria for exclusion/inclusion of data should be clearly stated • High-throughput sequence data should be uploaded before submission, with a private link for reviewers provided (these are available from both GEO and ArrayExpress) Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: For the data that was analyzed using mixed-effects models, no explicit power analysis was performed beforehand; sample size was determined in advance to be no fewer than 5-6 animals per group as is standard in the field.
For the in vivo data, power analysis from pilot experiments suggested n = 5 mice were required to detect a difference in temperature threshold for seizure induction of 1 degree C (for α = 0.05; power = 0.8). This preliminary data was not shown.
The "n" for all experiments (cells; imaging fields; slices; mice) is included in the Results and/or Figure legend.

Statistical reporting
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Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: (For large datasets, or papers with a very large number of statistical tests, you may upload a single table file with tests, Ns, etc., with reference to sections in the manuscript.)

Group allocation
• Indicate how samples were allocated into experimental groups (in the case of clinical studies, please specify allocation to treatment method); if randomization was used, please also state if restricted randomization was applied • Indicate if masking was used during group allocation, data collection and/or data analysis Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: Additional data files ("source data") • We encourage you to upload relevant additional data files, such as numerical data that are represented as a graph in a figure, or as a summary table • Where provided, these should be in the most useful format, and they can be uploaded as "Source data" files linked to a main figure or table The "n" for all experiments refers to independent biological replication. We explicitly report detailed information regarding the number of cells, imaging fields, slices, and animals, used for all imaging experiments, to make clear what replicates may have shared sources of variability.
There was no exclusion of outliers.
Criteria for data exclusion/ inclusion is stated clearly in the Results and in the Methods sections.
All statistical analysis methods are described and justified. Raw data is presented in figures whenever feasible. P-values are reported exactly. This information can be found in the results, figure legends, and methods sections.
Samples were allocated into groups by genotype and mouse age. Masking was used during data collection (as feasible), and for all data analysis. • Include model definition files including the full list of parameters used • Include code used for data analysis (e.g., R, MatLab) • Avoid stating that data files are "available upon request" Please indicate the figures or tables for which source data files have been provided: All data generated or analyzed during this study are included in the manuscript and supporting files and has been uploaded to the data repository G-Node here: https://gin.g-node.org/GoldbergNeuroLab/ Mattis-et-al-2022 Source data for all Figures (1-8) and Tables (1-2) is listed below has been included in the supplementary files, uploaded to G-Node, and has been made available without restriction. This includes: - Figure 1: quantification of spikes, associated with panels G, H, I - Figure Table 1: data summary and raw data files for all quantification of DG GC intrinsic properties - Table 2: data summary and raw data files for all quantification of DG GC intrinsic properties Code used for analysis is also provided via a GitHub repository: https://github.com/GoldbergNeuroLab/Mattis-et-al.
-2022 -Matlab code used for analysis of 2P imaging data -Matlab and Python code used for analysis of single cell electrophysiologic data -Python code used for quantification of EPSCs and IPSCs