FERONIA Kinase-Interacting Cell Wall Sensors LRX1/2 Regulate the Plant Rhizosphere Microbiome
Bibliographic record
Abstract
Plants establish beneficial associations with microbiota, enhancing their resilience to environmental challenges. FERONIA (FER) kinase shapes the microbiome; despite extensive knowledge of FER interactors that regulate development and immunity against pathogens, the specific partners involved in microbiome modulation remain underexplored. Through a reverse genetic screen of Arabidopsis leucine-rich repeat extensin ( LRX) genes, which encode FER-interacting cell wall sensors, we found that loss of function of lrx1/2 leads to enriched rhizosphere Pseudomonas, similar to fer mutants. 16S rRNA sequencing revealed that, when grown in natural soil, lrx1/2 and fer-4 have similarly altered rhizosphere microbiomes with decreased bacterial diversity. Notably, lrx1/2 and fer-4 mutants both exhibit growth defects in high pH natural soil that could be rescued by lowering the soil pH and increasing phosphate. Microbiome sequencing under conditions that rescued fer-4 and lrx1/2 stunting showed that the altered microbiome of lrx1/2 and fer-4 persists independently of changes in plant growth. This indicates that FER and LRX1/2 play an integral role in shaping the rhizosphere microbiome. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".