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Machine Learning Resolves Functional Phenotypes and Therapeutic Responses in KCNQ2 Developmental Epileptic

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    Pathogenic KCNQ2 variants cause KCNQ2-developmental and epileptic encephalopathy (KCNQ2-DEE) by enhancing SK channels. Machine learning on iPSC-derived neurons reveals irregular firing and bursting as biomarkers, with retigabine showing variable efficacy.

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    Area of Science:

    • Neuroscience
    • Genetics
    • Stem Cell Biology

    Background:

    • Pathogenic KCNQ2 variants lead to KCNQ2-developmental and epileptic encephalopathy (KCNQ2-DEE), a severe neurological disorder with no effective treatments.
    • KCNQ2 encodes the KV7.2 potassium channel, crucial for neuronal excitability, but its precise role in KCNQ2-DEE pathophysiology is unclear.
    • Understanding KV7.2 dysfunction is essential for developing targeted therapies for KCNQ2-DEE.

    Purpose of the Study:

    • To investigate the pathophysiological mechanisms of KCNQ2-DEE using patient-derived neurons.
    • To identify functional neuronal phenotypes and biomarkers associated with KCNQ2-DEE.
    • To evaluate the therapeutic potential of KV7 activators in a precision medicine context.

    Main Methods:

    • Utilized human induced pluripotent stem cell (iPSC)-derived neurons from KCNQ2-DEE patients and CRISPR/Cas9-corrected isogenic controls.
    • Employed microelectrode arrays (MEAs) to record extracellular spikes from a large number of neurons over an extended period.
    • Applied supervised and unsupervised machine learning algorithms to analyze complex neuronal firing patterns and identify disease-specific features.

    Main Results:

    • Identified dyshomeostatic enhancement of Ca2+-activated small conductance potassium (SK) channels as a common mechanism in KCNQ2-DEE neurons.
    • Discovered irregular spike timing and enhanced bursting as functional biomarkers for KCNQ2-DEE, influenced by genetic background.
    • Demonstrated that retigabine, a KV7 activator, can rescue disease-associated phenotypes, although with variable efficacy.

    Conclusions:

    • SK channel upregulation is a critical mechanism underlying KCNQ2-DEE.
    • MEA recordings combined with machine learning provide a powerful platform for dissecting KCNQ2-DEE phenotypes and identifying biomarkers.
    • This approach facilitates the evaluation of precision medicine interventions for KCNQ2-DEE in personalized neuronal models.