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Pruess, H.

Publications and source records attributed to Pruess, H..

2 recordsLinked to original sources

Encephalitis patient derived monoclonal GABAA receptor antibodies cause catatonia and epileptic seizures

Autoantibodies targeting the GABAA receptor (GABAAR) hallmark an autoimmune encephalitis presenting with frequent seizures and psychomotor abnormalities. Their pathogenic role is still not well-defined, given the common overlap with further autoantibodies and the lack of patient derived monoclonal antibodies (mAbs). We cloned and recombinantly produced five affinity-maturated GABAAR IgG1 mAbs from cerebrospinal fluid cells, which bound to various epitopes involving 1 and {gamma}2 receptor subunits, with variable binding strength and partial competition. mAbs selectively reduced GABAergic currents in neuronal cultures without causing receptor internalization. Cerebroventricular infusion of GABAAR mAbs and Fab fragments into rodents induced a severe phenotype with catatonia, seizures and increased mortality, reminiscent of encephalitis patients symptoms. Our results prove direct functional effects of autoantibodies on GABAARs and provide an animal model for GABAAR encephalitis. They further provide the scientific rationale for clinical treatments using antibody depletion and pave the way for future antibody-selective immunotherapies.

neuroscience

State-dependent signatures of Anti-NMDA-Receptor Encephalitis: a dynamic functional connectivity study

ObjectiveTraditional static functional connectivity (FC) analyses have shown functional network alterations in patients with anti-NMDA receptor encephalitis (NMDARE). Here, we use a dynamic FC approach that increases the temporal resolution of connectivity analyses from minutes to seconds. We hereby explore the spatiotemporal variability of large-scale brain network activity in NMDARE and assess the discriminatory power of functional brain states in a supervised classification approach. MethodsWe included resting-state fMRI data from 57 patients and 61 controls to extract four discrete connectivity states and assess state-wise group differences in FC, dwell time, transition frequency, fraction time and occurrence rate. Additionally, for each state, logistic regression models with embedded feature selection were trained to predict group status in a leave-one-out cross-validation scheme. ResultsCompared to controls, patients exhibited diverging dynamic FC patterns in three out of four states mainly encompassing the default-mode network and frontal areas. This was accompanied by a characteristic shift in the dwell time pattern and higher volatility of state transitions in patients. Moreover, dynamic FC measures were associated with disease severity, disease duration and positive and negative schizophrenia-like symptoms. Predictive power was highest in dynamic FC models and outperformed static analyses, reaching up to 78.6% classification accuracy. ConclusionsBy applying time-resolved analyses, we disentangle state-specific FC impairments and characteristic changes in temporal dynamics not detected in static analyses, offering new perspectives on functional reorganization underlying NMDARE. Correlation of dynamic FC measures with disease symptoms and severity indicates their clinical relevance and potential as prognostic biomarkers in NMDARE.

neuroscience