THE ROLE OF DIXDC1 IN AUTISM SPECTRUM DISORDER AND ESTABLISHING A HIGH-CONTENT PHENOTYPING PLATFORM FOR HUMAN ASD MODELS
Bibliographic record
Abstract
Autism spectrum disorder (ASD) describes a heterogeneous group of disorders with a worldwide prevalence of 1%. The two core symptoms of ASD are: 1) deficits in social communication and interaction and 2) restrictive and repetitive behaviours. Although the etiology of ASD remains unknown, environmental and genetic factors are believed to contribute to the disorder. Emerging studies suggest that molecules in the Wnt signalling pathway are important for the development of neural connectivity and are associated with ASD. We studied a Wnt signalling molecule named DIX domain containing-1 (DIXDC1) that has previously been linked to psychiatric diseases. We found that DIXDC1 regulates dendrite growth and dendritic spine formation and may function through a novel actin-dependent mechanism to regulate actin dynamics and polymerization. Our collaboration with Dr. Stephen Scherer (Hospital for Sick Children, Toronto) identified rare inherited genetic variants in DIXDC1. These variants caused an impairment of dendritic development. Furthermore, RNA sequencing identified signalling networks that were disrupted in Dixdc1 KO mouse brains, such as synaptic signalling, wnt signalling, and cell adhesion. These signalling networks are previously been implicated in ASD and we also identified different expressed genes that are strong candidate ASD-susceptibility genes. RNA sequencing also identified a novel genetic disorder that may be associated with Dixdc1 dysregulation, Ehlers-Danlos syndrome. Lastly, we developed a high-content imaging platform for automated imaging and analysis of human iPS-derived neurons modeling ASD models. We optimized culturing of human induced-neurons in a 96-well format, and immunostaining of synaptic markers to detect deficits in synapse function. Preliminary data shows that we are able to detect synaptic phenotypes in some ASD-associated iPS cell lines. Further development of the platform should be performed for the detection of a more robust synaptic phenotype and the potential discovery of drug treatments to rescue these phenotypes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".