Plant genetic and root-associated microbial diversity modulate Lactuca sativa responsiveness to a soil inoculum under phosphate deficiency
Bibliographic record
Abstract
Microbial-based approaches offer a promising strategy to decrease the use of chemical fertilizers in agriculture. Among them, arbuscular mycorrhizal fungi (AMF), which extend root surface area and enhance phosphate uptake, and phosphate-solubilizing bacteria (PSB) are particularly relevant. However, their effectiveness depends strongly on plant genetic diversity. To identify genetic markers underlying plant responses to beneficial soil microbes, we studied a panel of 128 fully sequenced Lactuca sativa varieties under controlled phosphate-starvation conditions and treated with AMF and PSB. Lettuce genetic variation showed a strong effect on physiological and morphological responses to microbial inoculation. Genome-wide association studies identified specific genomic regions associated with changes in leaf phosphate content and shoot biomass following treatment. Beyond genetic factors, we observed shifts in fungal β-diversity and increased bacterial α-diversity associated with phenotypic variation. We also identified 44 amplicon sequence variants associated with agriculturally relevant traits. Among these, six bacterial strains were experimentally validated through in vitro and pot experiments for their effects on leaf phosphate concentration and shoot biomass. Overall, we highlighted key genetic, microbial, and physiological mechanisms that may enhance microbial treatments for improved plant phosphate management in lettuce.
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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".