Plant miRNAs in the rhizosphere target microbial genes
Bibliographic record
Abstract
Abstract Background MicroRNAs (miRNAs) are small non-coding RNAs that control target gene expression, through sequence complementarity. Their roles in plants vary from regulating developmental processes to responding to abiotic and biotic stresses. 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. Thus, we hypothesised that the same process happens in the rhizosphere which contributes to shaping plant-associated microbial communities. Results To explore these questions, we performed small RNA sequencing in search of miRNAs in the rhizosphere of two evolutionarily distant plants, Arabidopsis thaliana and Brachypodium distachyon . This revealed the presence of specific and shared rhizospheric plant miRNAs, which were all absent in unplanted soils. A subset of these miRNAs were also detected inside rhizospheric bacteria, but were missing in bacteria from unplanted soils, suggesting bacterial uptake of surrounding plant miRNAs. Furthermore, an in silico analysis indicated potential targets of these rhizospheric miRNAs in plant-associated bacterial genomes. To examine the function of these miRNAs, A. thaliana mutants, affected in their miRNA and/or siRNA (small interfering RNA) biosynthesis, were grown. Their rhizospheric microbial communities were significantly disrupted in comparison with wild-type plants. Additionally, confronting synthetic rhizospheric miRNAs with bacterial cultures resulted in modulation of gene expression, suggesting a functional role of plant miRNAs in regulating microbial activity, in the rhizosphere. Conclusions This work makes an important contribution to the field of rhizospheric plant-microbe interactions and offers some significant insights into the potential of plant miRNAs for microbiota engineering.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".