Meta-metabolomic Responses of River Biofilms to Cobalt Exposure and Use of Dose-response Model Trends as an Indicator of Effects
Bibliographic record
Abstract
ABSTRACT Metabolites are low molecular-weight molecules produced during cellular metabolism. The global expression of the meta-metabolome (metabolomics at the community level) could thus potentially be used to characterize the exposure of an organism or a community to a specific stressor. Here, the meta-metabolomic fingerprints of mature biofilms were examined after 1, 3 and 7 days of exposure to five concentrations of cobalt (0, 1 x 10 - 7 , 1 x 10 - 6 , 5 x 10 - 6 and 1 x 10 - 5 M) in aquatic microcosms. The global changes in meta-metabolomic fingerprints were in good agreement with those of the other biological parameters studied (cobalt bioaccumulation, biomass, chlorophyll content). To better understand the dose-responses of the biofilm meta-metabolome, the untargeted LC-HRMS metabolomic data were further processed using the DRomics tool to build dose-response model curves and to calculate benchmark doses (BMD). These BMDs were aggregated into an empirical cumulative density function. A trend analysis of the metabolite dose-response curves suggests the presence of a concentration range inducing defense mechanisms (CRIDeM) between 4.7 x 10 - 7 and 2.7 x 10 - 6 M, and of a concentration range inducing damage mechanisms (CRIDaM) from 2.7 x 10 - 6 M to the highest Co concentration. The present study demonstrates that the molecular defense and damage mechanisms can be related to contaminant concentrations and represent a promising approach for environmental risk assessment of metals. SYNOPSIS This study focuses on the interpretation of the metabolite dose-response trends in river biofilms exposed to cobalt to identify concentration range inducing cellular mechanisms and improve the environmental risk assessment of metals.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".