Multiple mutualist effects on genomewide expression in the tripartite association between <i>Medicago truncatula,</i> nitrogen‐fixing bacteria and mycorrhizal fungi
Bibliographic record
Abstract
While all species interact with multiple mutualists, the fitness consequences and molecular mechanisms underlying these interactions remain largely unknown. We combined factorial ecological experiments with genomewide expression analyses to examine the phenotypic and transcriptomic responses of model legume Medicago truncatula to rhizobia and mycorrhizal fungi. We found synergistic effects of these mutualists on plant performance and examined unique features of plant gene expression responses to multiple mutualists. There were genomewide signatures of mutualists and multiple mutualists on expression, with partners often affecting unique sets of genes. Mycorrhizal fungi had stronger effects on plant expression than rhizobia, with 70% of differentially expressed genes affected by fungi. Fungal and bacterial mutualists had joint effects on 10% of differentially expressed genes, including unexpected, nonadditive effects on some genes with important functions such as nutrient metabolism. For a subset of genes, interacting with multiple mutualists even led to reversals in the direction of expression (shifts from up to downregulation) compared to interacting with single mutualists. Rhizobia also affected the expression of several mycorrhizal genes, including those involved in nutrient transfer to host plants, indicating that partner species can also impact each other's molecular phenotypes. Collectively, these data illustrate the diverse molecular mechanisms and transcriptional responses associated with the synergistic benefits of multiple mutualists.
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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".