Bioinformatics design of MiniPromoters for targeted delivery of expression
Bibliographic record
Abstract
Gene therapy has re-emerged as a viable treatment for rare genetic disorders. Key to recent progress in the field is non-insertional gene delivery using adeno-associated virus (AAV) vectors. Limited by the restricted payload capacity of AAV vectors, most AAV-based gene therapies use small, ubiquitous promoters. As a result, off-target expression of the therapeutic gene can occur, which may in turn have undesired side effects. In this context, designing selective promoters that restrict the expression of the therapeutic genes to the clinically relevant cells is an important research goal. In the past, we have shown the capacity to design compact, selective promoter sequences ( i.e. MiniPromoters) for targeting specific cells within the brain and eye. The design of a MiniPromoter was a manual process involving three steps: 1) literature search for a gene with restricted expression patterns in the target cells; 2) identification of the cis -regulatory regions (CRRs) of that gene ( i.e. promoter and enhancers); and 3) assembly of a subset of the gene’s CRRs into a MiniPromoter sequence. We are developing the OnTarget software to automate the design of MiniPromoters. It has three main components: 1) a Data Repository linking thousands of public processed experiments (CAGE, GRO-seq, ATAC-seq, DNase-seq, ChIP-seq, Hi-C, etc.) from multiple human primary cells, tissues and cell lines with in-house data collections (JASPAR, MANTA, allele-specific binding events, etc.); 2) a Selection Module for identifying and selecting CRRs of human genes in cell/tissue-specific contexts; and 3) a Design Module for fine-tuning the identified CRRs and modifying transcription factor binding sites within these CRRs in order to modulate the amount of delivered expression.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".