Global expression profiling of <i>Chlamydomonas reinhardtii</i> exposed to trace levels of free cadmium
Bibliographic record
Abstract
In the natural environment, cadmium is often found as a trace contaminant. Due to the complexity of Cd speciation and the heterogeneity of natural systems and processes, it is often difficult to determine clear relationships between analytical measurements of Cd and its induced biological response. Measurements of gene induction can be used to identify molecular mechanisms underlying toxicity and to quantify sublethal responses to trace contaminants. In the present paper, genes that could be involved in the tolerance of Cd to green algae were examined using two global transcriptome profiling strategies. Microarray and differential display techniques were used for a global transcriptome analysis of Chlamydomonas reinhardtii exposed to micromolar and lower Cd(2+) concentrations for a short period (2 h). Real-time quantitative polymerase chain reaction analysis confirmed that a small set of 10 genes was differentially expressed in response to trace Cd(2+) exposures ranging from 7.8 nM to 9.0 microM. Since induction was only observed for a few genes, none of which are known to function in a general stress response, it was likely the result of relevant responses to Cd exposure. The identified genes are discussed with respect to their possible involvement in Cd tolerance and to their future use as biomarkers for monitoring Cd bioavailability in natural soils and waters.
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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) | 0.000 | 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 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".