Characterization of the Huntingtin gene promoter and Huntingtin transcriptional regulation
Bibliographic record
Abstract
Huntington’s disease (HD) is a late onset, neurological, autosomal dominant genetic disorder. Despite being associated to a defined genetic mutation within the huntingtin gene (HTT), little is known about its transcriptional regulation. HTT is expressed, at varying levels, throughout the body. At the current time, the transcriptional regulation mechanisms controlling this differential expression pattern are unknown. Previous studies have focused on the genomic region directly preceding HTT’s transcriptional start site. The purpose of this thesis was to utilize the current understanding of mammalian transcriptional regulation to further characterize the HTT promoter and to expand the search for transcriptional regulatory regions outside the promoter. To direct this search a bioinfomatic screen was conducted, which identified 11 putative regions. Potential transcription factor binding sites (TFBSs) within these regions were identified through the use of available chIP-seq datasets. Curation of the TFBSs within the putative regions lead to selection of the 9th region, in addition to the promoter, for further study. To test the functionality of region 9 and identified candidate transcription factors (TFs), a panel of human kidney and rat neuronal cell lines were established. These cell lines stably expressed either the HTT promoter or region 9 luciferase constructs. Candidate TFs were tested using siRNA mediated knockdown. Knockdown of selected candidate TFs did not modulate HTT promoter function. The role of DNA methylation on transcriptional regulation of HTT was also explored using the Illumina 450K Methylation Array. Tissue specific DNA methylation of HTT using human cortex and liver tissues identified 33 differentially methylated sites. The role of the HD mutation on local and global DNA methylation was also investigated, finding no changes to local DNA and 15 differentially methylated regions globally. In conclusion, a data driven bioinfomatic search has expanded potential regulatory regions beyond that of the promoter of the HTT gene. A first attempt at identifying crucial TFs involved in HTT regulation was not successful, however additional candidates remain to be tested. A role for DNA methylation in tissue specific regulation of HTT has been identified, while the HD mutation itself does not appear to affect HTT DNA methylation.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".