Bibliographic record
Abstract
Gene expression is a highly regulated process that is essential to produce diverse proteins and RNAs required for life.The transfer of genetic information from gene to protein involves many steps including transcription of DNA to pre-mRNA, pre-mRNA processing, mRNA export and translation.Errors in each of these processes can be detrimental.Damage to DNA and transcriptional stress lead to activation of well-characterized transcriptional responses.We investigated the cellular response to inhibitors of pre-mRNA splicing.We hypothesized that pre-mRNA splicing stress, like transcriptional stress, would lead to the activation of a transcriptional response.To induce splicing stress, we used isoginkgetin (IGG), a small molecule inhibitor of spliceosome assembly.We treated HCT116 cells with IGG and completed microarray analysis to identify differentially expressed transcripts induced by IGG.Pathway enrichment analysis identified three enriched pathways, two of which involved the ATF4 transcription factor.ATF4 is a component of integrated stress response (ISR), an adaptive stress response to multiple cellular stresses.We measured multiple characteristics of ISR activation including ATF4 and ATF4 dependent transcript activation and found that IGG activated ATF4 and ATF4 dependent transcripts.We used CRISPR cas9 gene editing to create an ATF4 deficient cell line and completed RNA sequencing (RNA-seq) comparing the transcriptional response of ATF4-deficient and parental cells to IGG, using thapsigargin (Tg) as a positive control for ATF4 activation.We found that the IGG response is almost entirely ATF4-dependent with only 8 of 76 differentially expressed transcripts responding similarly in parental and ATF4deficient cells while most of the ATF4-dependent transcripts were also responsive to Tg.Transcriptional and splicing stress are known to elicit cell cycle arrests.Therefore, we
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".