Influence of Temperature and Nickel on Algal Biofilm Fatty Acid Composition
Bibliographic record
Abstract
Abstract Freshwater biofilms play an important role in aquatic ecosystems and are widely used to evaluate environmental conditions. Little is known about the effects of temperature and metals on biofilm fatty acid composition. In the present study, we exposed a natural biofilm cultured in mesocosms to a gradient of nickel (Ni) concentrations at 15 and 21 °C for 28 d. Metal bioaccumulation, algal taxonomic composition, and biofilm fatty acid profiles were determined. At both temperatures, bioaccumulated Ni increased with Ni exposure concentration and reached the highest values at 25 µM Ni, followed by a decrease at 55 and 105 µM Ni. In control biofilms, palmitic acid (16:0), palmitoleic acid (16:1n7), oleic acid (18:1n9), linoleic acid (18:2n6), and linolenic acid (18:3n3) were the dominant fatty acids at 15 and 21 °C. This composition suggests a dominance of cyanobacteria and green algae, which was subsequently confirmed by microscopic observations. The increase in temperature resulted in a decrease in the ratio of unsaturated to saturated fatty acids, which is considered to be an adaptive response to temperature variation. Polyunsaturated fatty acids (PUFAs) tended to decrease along the Ni gradient, as opposed to saturated fatty acids which increased with Ni concentrations. Temperature and Ni affected differently the estimated desaturase and elongase activities (product/precursor ratios). The increase in PUFAs at 15 °C was concomitant to an increase in Δ9-desaturase (D9D). The estimated activities of D9D, Δ12-desaturase, and Δ15-desaturase decreased along the Ni gradient and reflected a decline in PUFAs. The elevated estimated elongase activity reflected the observed increase in saturated fatty acids at the highest Ni exposure concentration (105 µM). Our results suggest that fatty acids could be used as an endpoint to evaluate environmental perturbations. Environ Toxicol Chem 2020;39:1566–1577. © 2020 SETAC Abstract The increase in temperature was accompanied by a decrease in the degree of unsaturation. Exposure to nickel also induced a decrease in polyunsaturated fatty acids. The response of biofilms thus revealed and additive effect of the two stressors. Our results highlighted the pertinence of using fatty acids as an endpoint to evaluate environmental perturbations. PUFA = polyunsaturated fatty acid; SFA = saturated fatty acid; UFA = unsaturated fatty acid.
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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 | 0.000 |
| 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".