A window into the transcriptomic basis of genotype‐by‐genotype interactions in the legume–rhizobia mutualism
Bibliographic record
Abstract
The maintenance of genetic variation in the benefits provided by mutualists is an evolutionary puzzle (Heath & Stinchcombe, ). Over time, natural selection should favour the benefit strategy that confers the highest fitness, eroding genetic variation in partner quality. Yet abundant genetic variation in partner quality exists in many systems (Heath & Stinchcombe, ). One possible resolution to this puzzle is that the genetic identity of both a host and its partner affects the benefits each mutualist provides to the other, a pattern known as a genotype-by-genotype interaction (Figure ). Mounting evidence suggests that genotype-by-genotype interactions between partners are pervasive at the phenotypic level (Barrett, Zee, Bever, Miller, & Thrall, ; Heath, ; Hoeksema & Thompson, ). Ultimately, however, to link these phenotypic patterns to the maintenance of genetic variation in mutualisms we need to answer two questions: How much variation in mutualism phenotypes is attributable to genotype-by-genotype interactions, and what mutualistic functions are influenced by each partner and by the interaction between their genomes? In this issue of Molecular Ecology, Burghardt et al. (2017) use transcriptomics to address both questions in the legume-rhizobia mutualism.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".