Gene-based analysis identifies novel microRNA candidates for Autism Spectrum Disorder
Bibliographic record
Abstract
Abstract Background Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with unclear physiopathology. Biomarker-based diagnostic tools for early detection and targeted treatments have yet to be developed. MicroRNAs (miRNAs), which are critical regulators of genes involved in brain development and function, are emerging as promising candidates as molecular mechanisms, biomarkers, and therapeutic targets for ASD. This study aimed to identify miRNAs involved in ASD and evaluate their potential functions through in silico analysis. Methods A comprehensive analysis was performed to find genomic variants with a predicted functional impact on miRNA activity. Using large datasets of Single Nucleotide Variants (SNVs, N = 4300) and Copy Number Variants (CNVs, N = 3570) from ASD cases, we identified miRNAs targeted by CNVs or containing SNVs predicted to disrupt their function in ASD subjects, and compared their frequencies with controls. Selected regulatory miRNA-mRNA networks and their associated biological functions were characterized. Results Seventy mature miRNAs were identified, including both previously reported and novel ASD candidates, predicted to regulate 2742 brain-expressed genes. Enrichment analysis revealed their involvement in cellular signaling, transcription regulation, protein metabolism, and chromatin organization – biological processes strongly related to ASD. Notably, 63% of these miRNAs are predicted to target 71 known ASD risk genes and the KCNB1, MECP2, NCKAP1 and ZBTB20 genes are each regulated by at least four miRNAs. Conclusions This gene-based analysis identified miRNAs regulating gene networks and biological pathways implicated in brain function and plasticity, frequently disrupted in ASD. These findings strengthen the evidence for miRNA involvement in ASD, paving the way for novel diagnostic and therapeutic strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".