Natural genetic transformation of the wheat rhizosphere microbial community through DNA inoculations
Bibliographic record
Abstract
Abstract Rhizosphere microorganisms are known to be able to modify the plant’s ability to resist abiotic stresses. It is, however, difficult to modify microbial communities to improve plant phenotypes. Here we tested if a rhizosphere microbial community from a water-stress naïve soil could be modified by adding DNA extracted from soils with a water stress history. Six-week-old wheat plants growing under low or high-water availabilities were inoculated with DNA extracted from soils with contrasting long-term histories of water availability – one continuously and the other intermittently exposed to water deficit. The fate of the inoculated DNA in the rhizosphere microbial communities was assessed by shotgun metagenomics. Putatively transferred inoculum genes were disproportionately found in the Acidobacteria and Bacteroidetes and belonged to functional category such as antibiotics, biofilm, and carboxylates metabolism, among others. These functional categories were shared by pre-inoculation laterally transferred genes in the recipient soil, highlighting their usefulness for life in soil. The “continuous” inoculum reduced the stress levels of wheat under reduced soil water content, suggesting that the natural genetic transformation of the rhizosphere community can feedback to the plant. Altogether, we are providing evidence for an ecological mechanism that could be harnessed to modify plant-associated microbial communities and help plants sustain water stress.
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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 | 0.001 |
| 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".