Human iPSC-derived glutamatergic neurons with pathogenic KCNQ2 variants display hyperactive bursting phenotypes
Bibliographic record
Abstract
Pathogenic variants in the KCNQ2 gene, which encodes a potassium channel subunit, are associated with neonatal seizures, epileptic encephalopathy, intellectual disability, and autism. Although the consequences of disrupted KCNQ2 channel function have been studied in the past, the detailed molecular mechanisms underlying the development of neurological phenotypes remain unclear, and neuronal models of specific patient variants are lacking. We generated patient-specific induced pluripotent stem cells (iPSCs) from fibroblasts from three individuals with distinct KCNQ2 pathogenic variants. We corrected the KCNQ2 variants using CRISPR-Cas9 editing to create isogenic controls and differentiated these iPSCs into glutamatergic neurons to study the effects of each variant on neuronal function. The three KCNQ2 variants were: 1) KCNQ2 c.875_877delTCCinsCCT, L292_L293delinsPF, 2) KCNQ2 c.766G > T, G256W, and 3) KCNQ2 c.821C > T, T274M. Our data revealed longer neurite outgrowth in two patient lines (T274M and G256W). Transcriptional profiling showed that all three KCNQ2 lines co-expressed genes enriched in synaptic transmission/signaling, cell adhesion, and GTPase signal transduction. Functional analyses of neuronal networks revealed increased burst duration in all three KCNQ2 lines compared with their isogenic controls. Furthermore, neurons from the L292_L293delinsPF and T274M lines displayed increased network connectivity associated with increased density of synaptic markers. Finally, we detected hyperexcitable neuronal networks in the G256W line with electrical stimulation of the neural networks on a high-density microelectrode array, and this phenotype was rescued with retigabine. These disease-related phenotypes for each of the KCNQ2 pathogenic variants can be used for drug screening to identify treatment options for the patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".