Mobile gene clusters and coexpressed plant–rhizobium pathways drive partner quality variation in symbiosis
Bibliographic record
Abstract
Plant–microbe symbioses such as the legume–rhizobium mutualism are vital in the web of ecological relationships within both natural and managed ecosystems, influencing primary productivity, crop yield, and ecosystem services. The outcome of these interactions for plant hosts varies quantitatively and can range from highly beneficial to even detrimental depending on natural genetic variation in microbial symbionts. Here, we take a systems genetics approach, harnessing the genetic diversity present in wild rhizobial populations to predict genes and molecular pathways crucial in determining partner quality, i.e., the benefits of symbiosis for legume hosts. We combine traits, dual-RNAseq of both partners from active nodules, pangenomics/pantranscriptomics, and Weighted Gene Co-expression Network Analysis (WGCNA) for a panel of 20 Sinorhizobium meliloti strains that vary in symbiotic partner quality. We find that genetic variation in the nodule transcriptome predicts host plant biomass, and WGCNA reveals networks of genes in plants and rhizobia that are coexpressed and associated with high-quality symbiosis. Presence–absence variation of gene clusters on the symbiosis plasmid (pSymA), validated in planta, is associated with high or low-quality symbiosis and is found within important coexpression modules. Functionally our results point to management of oxidative stress, amino acid and carbohydrate transport, and NCR peptide signaling mechanisms in driving symbiotic outcomes. Our integrative approach highlights the complex genetic architecture of microbial partner quality and raises hypotheses about the genetic mechanisms and evolutionary dynamics of symbiosis.
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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 | 0.001 | 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".