Soil contamination alters the willow root and rhizosphere metatranscriptome and the root–rhizosphere interactome
Why this work is in the frame
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Bibliographic record
Abstract
Phytoremediation using willows is thought to be a sustainable alternative to traditional remediation techniques involving excavation, transport, and landfilling. However, the complexity of the interaction between the willow and its associated highly diverse microbial communities makes the optimization of phytoremediation very difficult. Here, we have sequenced the rhizosphere metatranscriptome of four willow species and the plant root metatranscriptome for two willow species growing in petroleum hydrocarbon-contaminated and non-contaminated soils on a former petroleum refinery site. Significant differences in the abundance of transcripts related to different bacterial and fungal taxa were observed between willow species, mostly in contaminated soils. When comparing transcript abundance in contaminated vs. non-contaminated soil for each willow species individually, transcripts for many microbial taxa and functions were significantly more abundant in contaminated rhizosphere soil for Salix eriocephala, S. miyabeana and S. purpurea, in contrast to what was observed in the rhizosphere of S. caprea. This agrees with the previously reported sensitivity of S. caprea to contamination, and the superior tolerance of S. miyabeana and S. purpurea to soil contamination at that site. The root metatranscriptomes of two species were compared and revealed that plants transcripts are mainly influenced by willow species, while microbial transcripts mainly responded to contamination. A comparison of the rhizosphere and root metatranscriptomes in the S. purpurea species revealed a complete reorganization of the linkages between root and rhizosphere pathways when comparing willows growing in contaminated and non-contaminated soils, mainly because of large shifts in the rhizosphere metatranscriptome.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 it