Rhizospheric miRNAs affect the plant microbiota
Bibliographic record
Abstract
Abstract Small ribonucleic acids (RNAs) have been shown to play important roles in cross-kingdom communication, notably in plant–pathogen relationships. Plant micro RNAs (miRNAs)—one class of small RNAs—were even shown to regulate gene expression in the gut microbiota. Plant miRNAs could also affect the rhizosphere microbiota. Here we looked for plant miRNAs in the rhizosphere of model plants, and if these miRNAs could affect the rhizosphere microbiota. We first show that plant miRNAs were present in the rhizosphere of Arabidopsis thaliana and Brachypodium distachyon. These plant miRNAs were also found in or on bacteria extracted from the rhizosphere. We then looked at the effect these plants miRNAs could have on two typical rhizosphere bacteria, Variovorax paradoxus and Bacillus mycoides. The two bacteria took up a fluorescent synthetic miRNA but only V. paradoxus shifted its transcriptome when confronted to a mixture of six plant miRNAs. V. paradoxus also changed its transcriptome when it was grown in the rhizosphere of Arabidopsis that overexpressed a miRNA in its roots. As there were differences in the response of the two isolates used, we looked for shifts in the larger microbial community. We observed shifts in the rhizosphere bacterial communities of Arabidopsis mutants that were impaired in their small RNA pathways, or overexpressed specific miRNAs. We also found differences in the growth and community composition of a simplified soil microbial community when exposed in vitro to a mixture of plant miRNAs. Our results support the addition of miRNAs to the plant tools shaping rhizosphere 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 | 0.000 |
| 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".