Rhizospheric miRNAs affect the plant microbiota
Bibliographic record
Abstract
Abstract Recently, small RNAs have been shown to play important roles in cross-kingdom communication, notably in plant-pathogen relationships. Plant miRNAs were even shown to regulate gene expression in the gut microbiota. But what impact do they have on the plant microbiota? Here we hypothesized that plant miRNAs can be found in the rhizosphere of plants, where they are taken up by rhizosphere bacteria, influencing their gene expression, thereby shaping the rhizosphere bacterial community. We found plant miRNAs in the rhizosphere of Arabidopsis thaliana and Brachypodium distachyon . These plant miRNAs were also found in rhizosphere bacteria, and fluorescent synthetic miRNAs were taken up by cultivated bacteria. A mixture of five plant miRNAs modulated the expression of more than a hundred genes in Variovorax paradoxus , whereas no effect was observed in Bacillus mycoides . Similarly, when V. paradoxus was grown in the rhizosphere of Arabidopsis that overexpressed a miRNA, it changed its gene expression profile. The rhizosphere bacterial communities of Arabidopsis mutants that were impaired in their miRNA or small RNA pathways differed from wildtype plants. Similarly, bacterial communities of Arabidopsis overexpressing specific miRNAs diverged from control plants. Finally, the growth and the abundance of specific ASVs of a simplified soil community were affected by exposure to a mixture of synthetic plant miRNAs. Taken together, our results support a paradigm shift in plant-bacteria interactions in the rhizosphere, adding miRNAs to the plant tools shaping microbial assembly.
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) | 0.000 | 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".