Meta-transcriptomics indicates biotic cross-tolerance in willow trees cultivated on petroleum hydrocarbon contaminated soil
Bibliographic record
Abstract
BACKGROUND: High concentrations of petroleum hydrocarbon (PHC) pollution can be hazardous to human health and leave soils incapable of supporting agricultural crops. A cheap solution, which can help restore biodiversity and bring land back to productivity, is cultivation of high biomass yielding willow trees. However, the genetic mechanisms which allow these fast-growing trees to tolerate PHCs are as yet unclear. METHODS: Salix purpurea 'Fish Creek' trees were pot-grown in soil from a former petroleum refinery, either lacking or enriched with C10-C50 PHCs. De novo assembled transcriptomes were compared between tree organs and impartially annotated without a priori constraint to any organism. RESULTS: Over 45% of differentially expressed genes originated from foreign organisms, the majority from the two-spotted spidermite, Tetranychus urticae. Over 99% of T. urticae transcripts were differentially expressed with greater abundance in non-contaminated trees. Plant transcripts involved in the polypropanoid pathway, including phenylalanine ammonia-lyase (PAL), had greater expression in contaminated trees whereas most resistance genes showed higher expression in non-contaminated trees. CONCLUSIONS: The impartial approach to annotation of the de novo transcriptomes, allowing for the possibility for multiple species identification, was essential for interpretation of the crop's response treatment. The meta-transcriptomic pattern of expression suggests a cross-tolerance mechanism whereby abiotic stress resistance systems provide improved biotic resistance. These findings highlight a valuable but complex biotic and abiotic stress response to real-world, multidimensional contamination which could, in part, help explain why crops such as willow can produce uniquely high biomass yields on challenging marginal land.
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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".