Transcriptomic points of departure for 6PPD-Quinone derived from human Caco-2 and HepG2 cells
Bibliographic record
Abstract
N-(1,3-Dimethyl butyl)- N ′-phenyl-phenylenediamine-quinone (6ppd-quinone) is of emerging concern due to its widespread presence and toxicity to aquatic species. The chemical has been detected in human biofluids though little is known about its effects on human tissues. The objective of this study was to increase understanding of 6ppd-quinone's potential human health effects by deriving transcriptomic points of departure (tPOD) values in two human cell lines using the TPD-seq workflow. An EC20 for cytotoxicity was calculated for Caco-2 (104 μg/L) but not for HepG2 cells. Even in the absence of cytotoxicity, tPOD values (20th gene, 10th percentile, mode) were calculated in Caco-2 (6.5-25 μg/L) and HepG2 (0.36-35 μg/L) cells. These ranges capture values from 16 statistical and bioinformatic tests that examined mapping methods (CLC and Deplexer), algorithms (Limma and DESeq2), and filters (log2FC and BMR). The most common and sensitive genes with calculable benchmark doses (BMDs) in Caco-2 (DPF2, CD44, PGAP1, GDF15, H4C16) and HepG2 (SLC5A3, DKK1, ARG2, PHLDA1, TM4SF1) cells are listed. Pathway BMDs were also calculated for Caco-2 (systemic lupus erythematosus, 9.7-18 μg/L; alcoholism, 9.7-20 μg/L; viral carcinogenesis, 9.3–18.1 μg/L), and HepG2 (metabolic pathways, 50-60 μg/L) cells. These findings highlight TPD-seq as an efficient workflow to yield quantitative and mechanistic data relevant for human health risk assessment. • 6ppd-quinone is emerging chemical of concern albeit with limited human health data. • TPD-seq workflow provides quantitative and informative molecular data efficiently. • tPODs were calculated for Caco-2 and HepG2 cells exposed to 6ppd-quinone. • Perturbed genes and biological pathways provide mechanistic insights.
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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) | 0.001 | 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 | 0.001 | 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".