Differential Expression Profiling of the Hepatic Proteome in a Rat Model of Dioxin Resistance
Bibliographic record
Abstract
One characteristic feature of acute 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) toxicity is dramatic interspecies and interstrain variability in sensitivity. This complicates dioxin risk assessment for humans. However, this variability also provides a means of characterizing mechanisms of dioxin toxicity. Long-Evans (Turku/AB) rats are orders of magnitude more susceptible to TCDD lethality than Han/Wistar (Kuopio) rats, and this difference constitutes a very useful model for identifying mechanisms of dioxin toxicity. We adopted a proteomic approach to identify the differential effects of TCDD exposure on liver protein expression in Han/Wistar rats as compared with Long-Evans rats. This allows determination of which, if any, protein markers are indicative of differences in dioxin susceptibility and/or responsible for conferring resistance. Differential protein expression in total liver protein was assessed using two-dimensional gel electrophoresis, computerized gel image analysis, in-gel digestion, and mass spectrometry. We observed significant changes in the abundance of several proteins, which fall into three general classes: (i) TCDD-independent and exclusively strain-specific (e.g. isoforms of the protein-disulfide isomerase A3, regucalcin, and agmatine ureohydrolase); (ii) strain-independent and only dependent on TCDD exposure (e.g. aldehyde dehydrogenase 3A1 and rat selenium-binding protein 2); (iii) dependent on both TCDD exposure and strain (e.g. oxidative stress-related proteins, apoptosis-inducing factor, and MAWD-binding protein). By integrating transcriptomic (microarray) data and genomic data (computational search of regulatory elements), we found that protein expression levels were mainly controlled at the level of transcription. These results reveal, for the first time, a subset of hepatic proteins that are differentially regulated in response to TCDD in a strain-specific manner. Some of these differential responses may play a role in establishing the major differences in TCDD response between these two strains of rats. As such, our work is expected to lead to new insights into the mechanism of TCDD toxicity and resistance.
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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 | 0.001 | 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".