Co-inoculation with novel nodule-inhabiting bacteria reduces the benefits of legume–rhizobium symbiosis
Bibliographic record
Abstract
The ecologically and economically vital symbiosis between nitrogen-fixing rhizobia and leguminous plants is often thought of as a bi-partite interaction, yet studies increasingly show the prevalence of non-rhizobial endophytes (NREs) that occupy nodules alongside rhizobia. Yet, what impact these NREs have on plant or rhizobium fitness remains unclear. Here, we investigated four NRE strains found to naturally co-occupy nodules of the legume Medicago truncatula alongside Sinorhizobium meliloti in native soils. Our objectives were to (1) examine the direct and indirect effects of NREs on M. truncatula and S. meliloti fitness, and (2) determine whether NREs can re-colonize root and nodule tissues upon reinoculation. We identified one NRE strain (522) as a novel Paenibacillus species, another strain (717A) as a novel Bacillus species, and the other two (702A and 733B) as novel Pseudomonas species. Additionally, we found that two NREs (Bacillus 717A and Pseudomonas 733B) reduced the fitness benefits obtained from symbiosis for both partners, while the other two (522, 702A) had little effect. Lastly, we found that NREs were able to co-infect host tissues alongside S. meliloti. This study demonstrates that variation of NREs present in natural populations must be considered to better understand legume–rhizobium dynamics in soil communities.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".