B18 Transcriptome profiling of B6.HttQ111/+ hepatocytes in response to chemical perturbagens
Bibliographic record
Abstract
Background We are interested in detecting the earliest molecular consequences of endogenous mutant Huntingtin expression. These early consequences can be difficult to detect when cells are in a steady state, so we are using chemical perturbations to investigate how mutant Huntingtin alters dynamic cellular responses. Our own data indicate that environmental perturbations, such as a high-fat diet, can induce widespread transcriptome modification in wild-type cells that is absent or unique in cells expressing mutant Huntingtin, making this approach useful to understanding the effects of mutant Huntingtin and identify potential therapeutic targets. Aims We aim to detect the early molecular consequences of endogenous mutant Huntingtin expression in B6.HttQ111/+ hepatocytes. Methods We are using two distinct perturbations to stress primary hepatocytes; the topoisomerase II inhibitor, Etoposide, to induce DNA damage, and the Carnitine Palmitoyl Transferase-1 inhibitor, Etomoxir, to inhibit fatty acid β-oxidation. Results/outcome We have generated transcriptomic profiles using RNAseq of samples collected across a dense time course of perturbagen exposure. These profiles reveal widespread transcriptional changes, which are consistent with the mechanisms of action of these agents. We are using these transcriptomic data to build in silico models of hepatocyte metabolism and response to DNA damage. We are currently validating our in silico models by measuring expression and activation of specific proteins predicted to be altered in these perturbed states. We will present cross-sectional data describing the impact of CAG expansion in Htt on transcriptional responses to chemical perturbation in primary hepatocytes. Conclusions We conclude that the transcriptome is altered in response to environmental perturbagens in B6.HttQ111/+ hepatocytes. Support CHDI foundation, Huntington Society of Canada
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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) | 0.002 | 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".