Integration of multi-omics and benchmark dose modeling to support adverse outcome pathways
Bibliographic record
Abstract
BACKGROUND: Recent advancements in omics and benchmark dose (BMD) modeling have facilitated identifying the dose required for a predetermined change in a response (e.g. gene or protein change) that can be used to establish acceptable dose levels for hazardous exposures. Adverse Outcome Pathways (AOPs) describe the causal links between toxicants and adverse effects through key events (KEs). Integrating omics data within the AOP framework quantitatively links early molecular events to later phenotypic effects. In this study, we use omic-based BMD analyses in an in vitro blood model exposed to radiation to identify point of departure (POD) values across KEs to acute myeloid leukemia (www.aopwiki.org/aop/432). METHODS: Isolated white blood cells were cultured and X-irradiated (1 Gy/minute, 0-6 Gy). Transcriptomic and proteomic changes were assessed 24 h post-exposure. BMD modeling was applied and significantly perturbed genes/proteins and pathways were identified. Those pathways relevant to KEs outlined in AOP 432 were grouped and a POD was determined. RESULTS: BMD modeling identified 1294 genes and 167 proteins with median BMD lower confident limit (BMD) values of 1.35 and 0.32 Gy, respectively. Pathway analysis identified biological processes related to DNA damage/repair, oxidative stress, cell cycle regulation, immune responses, and cancer development. These findings aligned with the KEs in AOP 432. The BMDL values of canonical pathways associated with these KEs were generally below 0.5 Gy with specific genes (e.g. GADD45A) displaying BMDLs <0.05 Gy. CONCLUSIONS: This work provides insights into predictive radiation induced mechanisms and associated dose of activity that can be taken into consideration for low dose (< 0.1 Gy) risk analysis.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".