Non-coding-regulatory regions of human brain genes delineated by bacterial artificial chromosome knock-in mice
Bibliographic record
Abstract
BACKGROUND: The next big challenge in human genetics is understanding the 98% of the genome that comprises non-coding DNA. Hidden in this DNA are sequences critical for gene regulation, and new experimental strategies are needed to understand the functional role of gene-regulation sequences in health and disease. In this study, we build upon our HuGX ('high-throughput human genes on the X chromosome') strategy to expand our understanding of human gene regulation in vivo. RESULTS: In all, ten human genes known to express in therapeutically important brain regions were chosen for study. For eight of these genes, human bacterial artificial chromosome clones were identified, retrofitted with a reporter, knocked single-copy into the Hprt locus in mouse embryonic stem cells, and mouse strains derived. Five of these human genes expressed in mouse, and all expressed in the adult brain region for which they were chosen. This defined the boundaries of the genomic DNA sufficient for brain expression, and refined our knowledge regarding the complexity of gene regulation. We also characterized for the first time the expression of human MAOA and NR2F2, two genes for which the mouse homologs have been extensively studied in the central nervous system (CNS), and AMOTL1 and NOV, for which roles in CNS have been unclear. CONCLUSIONS: We have demonstrated the use of the HuGX strategy to functionally delineate non-coding-regulatory regions of therapeutically important human brain genes. Our results also show that a careful investigation, using publicly available resources and bioinformatics, can lead to accurate predictions of gene expression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".