Back

Neural Extracellular Matrix Remodeling Signatures in Genetic and Acquired Mouse Models of Epilepsy

Blondiaux, A.; Jia, S.; Annamneedi, A.; Caliskan, G.; Schulze, J.; Montenegro-Venegas, C.; Wykes, R. C.; Fejtova, A.; Walker, M.; Stork, O.; Gundelfinger, E. D.; Dityatev, A.; Seidenbecher, C. I.

2023-04-19 neuroscience
10.1101/2023.04.19.537468 bioRxiv
Show abstract

Epilepsies are multifaceted neurological disorders characterized by abnormal brain activity, e.g., caused by imbalanced synaptic excitation and inhibition. The neural extracellular matrix (ECM) is dynamically modulated by physiological and pathophysiological activity and critically involved in controlling the brains excitability. We used different epilepsy models, i.e. mice lacking the presynaptic scaffolding protein Bassoon at excitatory, inhibitory or all synapse types as genetic models for rapidly generalizing early-onset epilepsy, and intra-hippocampal kainate injection, a model for acquired temporal lobe epilepsy, to study the relationship between epileptic seizures and ECM composition. Electroencephalogram recordings revealed Bassoon deletion at excitatory or inhibitory synapses having diverse effects on epilepsy-related phenotypes. While constitutive Bsn mutants and GABAergic neuron-specific knockouts (BsnDlx5/6cKO) displayed severe epilepsy with more and stronger seizures than kainate-injected animals, mutants lacking Bassoon solely in excitatory forebrain neurons (BsnEmx1cKO) showed only mild impairments. By semiquantitative immunoblotting and immunohistochemistry we show model-specific patterns of neural ECM remodeling, and we also demonstrate significant upregulation of the ECM receptor CD44 in null and BsnDlx5/6cKO mutants. ECM-associated WFA-binding chondroitin sulfates were strongly augmented in seizure models. Strikingly, Brevican, Neurocan, Aggrecan and link protein Hapln1 levels reliably predicted seizure properties across models, suggesting a link between ECM state and epileptic phenotype.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.