Allele-specific quantitation of ATXN3 and HTT transcripts in polyQ disease models
Bibliographic record
Abstract
BACKGROUND: The majority of genes in the human genome is present in two copies but the expression levels of both alleles is not equal. Allelic imbalance is an aspect of gene expression relevant not only in the context of genetic variation, but also to understand the pathophysiology of genes implicated in genetic disorders, in particular, dominant genetic diseases where patients possess one normal and one mutant allele. Polyglutamine (polyQ) diseases are caused by the expansion of CAG trinucleotide tracts within specific genes. Spinocerebellar ataxia type 3 (SCA3) and Huntington's disease (HD) patients harbor one normal and one mutant allele that differ in the length of CAG tracts. However, assessing the expression level of individual alleles is challenging due to the presence of abundant CAG repeats in the human transcriptome, which make difficult the design of allele-specific methods, as well as of therapeutic strategies to selectively engage CAG sequences in mutant transcripts. RESULTS: To precisely quantify expression in an allele-specific manner, we used SNP variants that are linked to either normal or CAG expanded alleles of the ataxin-3 (ATXN3) and huntingtin (HTT) genes in selected patient-derived cell lines. We applied a SNP-based quantitative droplet digital PCR (ddPCR) protocol for precise determination of the levels of transcripts in cellular and mouse models. For HD, we showed that the process of cell differentiation can affect the ratio between endogenous alleles of HTT mRNA. Additionally, we reported changes in the absolute number of the ATXN3 and HTT transcripts per cell during neuronal differentiation. We also implemented our assay to reliably monitor, in an allele-specific manner, the silencing efficiency of mRNA-targeting therapeutic approaches for HD. Finally, using the humanized Hu128/21 HD mouse model, we showed that the ratio of normal and mutant HTT transgene expression in brain slightly changes with the age of mice. CONCLUSIONS: Using allele-specific ddPCR assays, we observed differences in allele expression levels in the context of SCA3 and HD. Our allele-selective approach is a reliable and quantitative method to analyze low abundant transcripts and is performed with high accuracy and reproducibility. Therefore, the use of this approach can significantly improve understanding of allele-related mechanisms, e.g., related with mRNA processing that may be affected in polyQ diseases.
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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".