NF-κB Signaling Is Increased in HD3 Cells Following Exposure to 1,4-Benzoquinone: Role of Reactive Oxygen Species and p38-MAPK a
Bibliographic record
Abstract
In utero exposure to benzene, a known environmental contaminant, is associated with increased risk of leukemia. We have previously shown that in utero benzene exposure can alter the redox-sensitive transcription factor NF-κB, and we hypothesize that this is through benzene-induced reactive oxygen species (ROS) production interfering with the signaling pathway involving NF-κB and p38-Mitogen Activated Protein Kinase (MAPK). As benzoquinone (BQ) is one of benzene's most toxic metabolites, the objectives of this study were to determine whether ROS and p38-MAPK-mediated BQ-induced increased NF-κB activity. HD3 chicken erythroblast cells were transfected with an NF-κB luciferase-linked reporter plasmid and exposed to BQ (25 μM) for 2-24 h. NF-κB activities were determined through luciferase assays; Western blotting was conducted to assess changes in protein levels in nontransfected cells; and the presence of ROS was determined via 2',7'-dichlorodihydrofluorescein diacetate (DCFDA) flow cytometric assays. Results demonstrated that NF-κB activity was significantly increased following exposure to BQ for 16 and 24 h and DCFDA assays and pretreatment with antioxidants indicated that BQ-mediated ROS production was responsible for this increase. Furthermore, decreased inhibitor of kappaB-alpha (IκB-α) expression suggests that nuclear factor-kappaB (NF-κB) translocates into the nucleus and that p38-MAPK activation through a ROS-dependent pathway mediates BQ-mediated increases in NF-κB activity. Future studies investigating the role of p38-MAPK in this pathway are warranted. Evaluating the effects of toxicant exposure on cell signaling pathways is vital for understanding mechanisms of xenobiotic-induced toxicity.
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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 | 0.001 |
| 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".