High Conservation in Transcriptomic and Proteomic Response of White Sturgeon to Equipotent Concentrations of 2,3,7,8-TCDD, PCB 77, and Benzo[a]pyrene
Bibliographic record
Abstract
Adverse effects associated with exposure to dioxin-like compounds (DLCs) are mediated primarily through activation of the aryl hydrocarbon receptor (AHR). However, little is known about the cascades of events that link activation of the AHR to apical adverse effects. Therefore, this study used high-throughput, next-generation molecular tools to investigate similarities and differences in whole transcriptome and whole proteome responses to equipotent concentrations of three agonists of the AHR, 2,3,7,8-TCDD, PCB 77, and benzo[a]pyrene, in livers of a nonmodel fish, the white sturgeon (Acipenser transmontanus). A total of 926 and 658 unique transcripts were up- and down-regulated, respectively, by one or more of the three chemicals. Of the transcripts shared by responses to all three chemicals, 85% of up-regulated transcripts and 75% of down-regulated transcripts had the same magnitude of response. A total of 290 and 110 unique proteins were up- and down-regulated, respectively, by one or more of the three chemicals. Of the proteins shared by responses to all three chemicals, 70% of up-regulated proteins and 48% of down-regulated proteins had the same magnitude of response. Among treatments there was 68% similarity between the global transcriptome and global proteome. Pathway analysis revealed that perturbed physiological processes were indistinguishable between equipotent concentrations of the three chemicals. The results of this study contribute toward more completely describing adverse outcome pathways associated with activation of the AHR.
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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) | 0.001 | 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".