<i>CNTN5</i> <sup>−/+</sup> or <i>EHMT2</i> <sup>−/+</sup> iPSC-Derived Neurons from Individuals with Autism Develop Hyperactive Neuronal Networks
Bibliographic record
Abstract
Abstract Induced pluripotent stem cell (iPSC)-derived cortical neurons are increasingly used as a model to study developmental aspects of Autism Spectrum Disorder (ASD), which is clinically and genetically heterogeneous. To study the complex relationship of rare (penetrant) variant(s) and common (weaker) polygenic risk variant(s) to ASD, “isogenic” iPSC-derived neurons from probands and family-based controls, for modeling, is critical. We developed a standardized set of procedures, designed to control for heterogeneity in reprogramming and differentiation, and generated 53 different iPSC-derived glutamatergic neuronal lines from 25 participants from 12 unrelated families with ASD (14 ASD-affected individuals, 3 unaffected siblings, 8 unaffected parents). Heterozygous de novo (7 families; 16p11.2, NRXN1 , DLGAP2 , CAPRIN1 , VIP , ANOS1 , THRA ) and rare-inherited (2 families; CNTN5 , AGBL4 ) presumed-damaging variants were characterized in ASD risk genes/loci. In three additional families, functional candidates for ASD ( SET ), and combinations of putative etiologic variants ( GLI3/KIF21A and EHMT2/UBE2I combinations in separate families), were modeled. We used a large-scale multi-electrode array (MEA) as our primary high-throughput phenotyping assay, followed by patch clamp recordings. Our most compelling new results revealed a consistent spontaneous network hyperactivity in neurons deficient for CNTN5 or EHMT2. Our biobank of iPSC-derived neurons and accompanying genomic data are available to accelerate ASD research.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".