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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 teacher head, 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".