Loss of a plant receptor kinase recruits beneficial rhizosphere-associated <i>Pseudomonas</i>
Bibliographic record
Abstract
Abstract Maintaining microbiome structure is critical for the health of both plants 1 and animals 2 . In plants, enrichment of beneficial bacteria is associated with advantageous outcomes including protection from biotic and abiotic stress 3,4 . However, the genetic and molecular mechanisms by which plants enrich for specific beneficial microbes without general dysbiosis have remained elusive. Here we show that through regulation of NADPH oxidase, FERONIA kinase negatively regulates beneficial Pseudomonas fluorescens in the Arabidopsis rhizosphere microbiome. By rescreening a collection of Arabidopsis mutants that affect root immunity under gnotobiotic conditions, followed by microbiome sequencing in natural soil, we identified a FERONIA mutant ( fer-8 ) with a rhizosphere microbiome enriched in P. fluorescens without phylum-level dysbiosis. Using microbiome transplant experiments, we found that the fer-8 microbiome was beneficial and promoted plant growth. The effect of FER on rhizosphere Pseudomonads was independent of its immune coreceptor function, role in development, and jasmonic acid autoimmunity. We found that the fer-8 mutant has reduced basal levels of reactive oxygen species (ROS) in roots and that mutants deficient in NADPH oxidase showed elevated rhizosphere Pseudomonad levels. Overexpression of the ROP2 gene (encoding a client of FER and positive regulator of NADPH oxidase 5 ) in fer-8 plants suppressed Pseudomonad overgrowth. This work shows that FER -mediated ROS production regulates levels of beneficial Pseudomonads in the rhizosphere microbiome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.002 | 0.001 |
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".