Transcriptome profiling of developmental and xenobiotic responses in a keystone soil animal, the oligochaete annelid Lumbricus rubellus
Bibliographic record
Abstract
BACKGROUND: Natural contamination and anthropogenic pollution of soils are likely to be major determinants of functioning and survival of keystone invertebrate taxa. Soil animals will have both evolutionary adaptation and genetically programmed responses to these toxic chemicals, but mechanistic understanding of such is sparse. The clitellate annelid Lumbricus rubellus is a model organism for soil health testing, but genetic data have been lacking. RESULTS: We generated a 17,000 sequence expressed sequence tag dataset, defining ~8,100 different putative genes, and built an 8,000-element transcriptome microarray for L. rubellus. Strikingly, less than half the putative genes (43%) were assigned annotations from the gene ontology (GO) system; this reflects the phylogenetic uniqueness of earthworms compared to the well-annotated model animals. The microarray was used to identify adult- and juvenile-specific transcript profiles in untreated animals and to determine dose-response transcription profiles following exposure to three xenobiotics from different chemical classes: inorganic (the metal cadmium), organic (the polycyclic aromatic hydrocarbon fluoranthene), and agrochemical (the herbicide atrazine). Analysis of these profiles revealed compound-specific fingerprints which identify the molecular responses of this annelid to each contaminant. The data and analyses are available in an integrated database, LumbriBASE. CONCLUSION: L. rubellus has a complex response to contaminant exposure, but this can be efficiently analysed using molecular methods, revealing unique response profiles for different classes of effector. These profiles may assist in the development of novel monitoring or bioremediation protocols, as well as in understanding the ecosystem effects of exposure.
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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".