PSX-39 Late-Breaking Abstract: Characterization of epigenetic and transcriptional landscape in heat stressed rats using ATAC-seq and RNA-seq
Bibliographic record
Abstract
Abstract Understanding animal physiology and identifying reliable biomarkers may help to establish effective management strategies for the prevention of heat stress (HS). However, little is known about the molecular mechanism of mammal tolerance to high temperatures. In a previous study with Sprague-Dawley rats, we performed RNA-seq assays on the liver of rats in control (CT; 22 ℃, n = 5) and heat stress (HS120; 42 ℃ for 120 min, n = 5) groups. A total of 3,909 differential expression genes (DEGs, Q < 0.05) were observed. This study was conducted to further examine the epigenetic landscape in the liver of rats under HS and identify transcription factors (TFs), as well as their regulated genes. Three liver tissues were selected from the RNA-seq samples and performed an Assay for Transpose Accessible Chromatin (ATAC-seq). Peaks meeting criteria of P< 0.05 and |Fold Change| >1.5 were considered as differential peaks. All ATAC-seq libraries generated an expected distribution of the insert fragment lengths, with the majority of fragments being small, which characterize inter-nucleosomal open chromatin, and progressively fewer fragments of larger size, which are spanning nucleosomes. The accessibility of transcriptional start sites (TSS) was significantly enriched. After merging data, 2,356 differential peaks showed CT having more accessible TSS than H120 and only 230 differential peaks showed H120 group having more accessible TSS than CT. Thirty-six and 22 TF motifs were predicted by up- and down-regulated differential peaks in H120 vs. CT. Together with the previous DEG results, we proposed candidate TFs annotated to Cebpa, Foxa4, and Sp3 DEGs, which are involved in the regulation of oxidative stress. In summary, we showed that nuclear chromatin in the liver of heat stressed rats was less open than that of control rats. We suggest that the TFs (Cebpa, Foxa4, and Sp3) may be involved in the physiological regulation of HS.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".