Plant miRNAs influence soil bacterial growth and amino acid uptake, restructuring community composition
Bibliographic record
Abstract
modifies its root miRNA content when fertilized with a mixture of 17 amino acids. The miRNAs that responded to amino acid fertilization and other rhizosphere-abundant miRNAs were applied to a simplified soil community, grown with diverse amino acid sources, to test if they interfered with microbial community growth, community composition, and amino acid consumption. Plant miRNAs affected the community's growth in over 70% of the amino acid sources. The impact of plant miRNAs also depended on the N source supplied to the microbial community, with the strongest effect observed with L-lysine. Specifically, ath-miR159a reduced the microbial consumption of L-lysine, further supporting that plant miRNAs can influence microbial amino acid uptake. Plant miRNAs also strongly affected the relative abundance of specific bacterial taxa, which we subsequently isolated. These community shifts were explained by the subtle but robust impact of plant miRNAs on isolates' growth and, for two out of three isolates, on amino acid consumption. Surprisingly, while plant miRNAs inhibited amino acid consumption at both the community and isolate levels, the effects of plant miRNAs were mostly positive. Our results suggest that rhizospheric plant miRNAs might have a role in modulating the amino acid consumption of soil bacteria which reshapes the community, but not necessarily in a competitive framework.
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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".