Direction and modality of transcription changes caused by TAD boundary disruption in Slc29a3/Unc5b locus depends on tissue-specific epigenetic context
Bibliographic record
Abstract
BACKGROUND: Topologically associating domains (TADs) are believed to play a role in the regulation of gene expression by constraining or guiding interactions between the regulatory elements. While the impact of TAD perturbations is typically studied in developmental genes with highly cell-type-specific expression patterns, this study examines genes with broad expression profiles separated by a strong insulator boundary. We focused on the mouse Slc29a3/Unc5b locus, which encompasses two distinct TADs containing ubiquitously expressed and essential for viability genes. We disrupted the CTCF-boundary between these TADs and analyzed the resulting changes in gene expression. RESULTS: Deletion of four CTCF binding sites at the TAD boundary altered local chromatin architecture, abolishing pre‑existing loops and creating novel long‑range interactions that spanned the original TAD boundary. Using UMI-assisted targeted RNA-seq we evaluated transcriptional changes of Unc5b, Slc29a3, Psap, Vsir, Cdh23, and Sgpl1 across various organs. We found that TAD boundary disruption led to variable transcriptional responses, where not only the magnitude but also the direction of gene expression changes were tissue-specific. Current hypotheses on genome architecture function, such as enhancer competition and hijacking, as well as genomic deep learning models, only partially explain these transcriptional changes, highlighting the need for further investigation into the mechanisms underlying TAD function and gene regulation. CONCLUSIONS: Disrupting the insulator element between broadly expressed genes resulted in moderate, tissue-dependent transcriptional alterations, rather than uniformly activating or silencing the target genes. These findings show that TAD boundaries contribute to context‑specific regulation even at housekeeping loci and underscore the need for refined models to predict the effects of non‑coding structural variants.
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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 | 0.000 |
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".