Meta-metabolomic responses of river biofilms to cobalt exposure and use of dose-response model trends as an indicator of effects
Bibliographic record
Abstract
The response of the meta-metabolome is rarely used to characterize the effects of contaminants on a whole community. Here, the meta-metabolomic fingerprints of biofilms were examined after 1, 3 and 7 days of exposure to five concentrations of cobalt (from background concentration to 1 ×10-5 M) in aquatic microcosms. The untargeted metabolomic data were processed using the DRomics tool to build dose-response models and to calculate benchmark-doses. This approach made it possible to use 100% of the chemical signal instead of being limited to the very few annotated metabolites (7%). These benchmark-doses were further aggregated into an empirical cumulative density function. A trend analysis of the untargeted meta-metabolomic feature dose-response curves after 7 days of exposure suggested the presence of a concentration range inducing defense responses between 1.7 ×10-9 and 2.7 ×10-6 M, and of a concentration range inducing damage responses from 2.7 ×10-6 M and above. This distinction was in good agreement with changes in the other biological parameters studied (biomass and chlorophyll content). This study demonstrated that the molecular defense and damage responses can be related to contaminant concentrations and represents a promising approach for environmental risk assessment of metals. Assessing the level of stress induced by a contaminant on a microorganism community is a challenging task compared to studies involving a single species. Here, we propose an original approach based on untargeted metabolomic response that includes both annotated and unannotated metabolites. This novel data processing method uses the trends of untargeted meta-metabolite dose-response curves to identify concentration ranges of defense and damage responses of biofilms exposed to cobalt. These results open up a new way of interpreting omics data using the whole chemical signal without being restricted by annotation and a new tool for environmental risk assessment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".