Transcriptional changes in Huntington disease identified using genome-wide expression profiling and cross-platform analysis
Bibliographic record
Abstract
Evaluation of transcriptional changes in the striatum may be an effective approach to understanding the natural history of changes in expression contributing to the pathogenesis of Huntington disease (HD). We have performed genome-wide expression profiling of the YAC128 transgenic mouse model of HD at 12 and 24 months of age using two platforms in parallel: Affymetrix and Illumina. The data from these two powerful platforms were integrated to create a combined rank list, thereby revealing the identity of additional genes that proved to be differentially expressed between YAC128 and control mice. Using this approach, we identified 13 genes to be differentially expressed between YAC128 and controls which were validated by quantitative real-time PCR in independent cohorts of animals. In addition, we analyzed additional time points relevant to disease pathology: 3, 6 and 9 months of age. Here we present data showing the evolution of changes in the expression of selected genes: Wt1, Pcdh20 and Actn2 RNA levels change as early as 3 months of age, whereas Gsg1l, Sfmbt2, Acy3, Polr2a and Ppp1r9a RNA expression levels are affected later, at 12 and 24 months of age. We also analyzed the expression of these 13 genes in human HD and control brain, thereby revealing changes in SLC45A3, PCDH20, ACTN2, DDAH1 and PPP1R9A RNA expression. Further study of these genes may unravel novel pathways contributing to HD pathogenesis. DDBJ/EMBL/GenBank accession no: GSE19677.
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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 | 0.000 | 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".