Author Response: Repeatability of adaptation in sunflowers reveals that genomic regions harbouring inversions also drive adaptation in species lacking an inversion
Bibliographic record
Abstract
In plants, like in humans, DNA is arranged into sections known as genes that are in turn organised into structures called chromosomes. Mutations that modify the activity of these genes can help plant species to adapt to a new environment or to extreme conditions such as drought. However, successful adaptation often requires changes in many different genes. If these sets of genes are located close to each other on the same chromosome, any mutations will likely be passed onto the next generation together. If the genes are located further away, or even on different chromosomes, they may instead be inherited separately so that the next generation does not benefit as much from the adaptation. A chromosome inversion – when a segment of chromosome breaks off and reattaches the other way around – can increase the likelihood that sets of mutations on the same chromosome will be inherited together. Many previous studies have found that chromosome inversions tend to drive the ability of species to adapt to different environments by keeping together mutations that affect the same characteristics. However, it is not clear how inversions affect the repeatability of the adaptation, that is, if another group of closely related plants faced the same challenge in their environment would they evolve in the same way, or would they evolve a new response? To address this question, Soudi, Jahani et al. used a genetics approach known as a genome wide association study to explore how three closely related species of sunflower have adapted to their respective environments. Two of the species grow in various environments across the centre and west of the USA that are often hot and dry, whereas the third species is restricted to the more humid coastal plain of Texas, USA. The experiments found that a few key genes had changed in all three sunflower species. However, each species also had mutations in a larger set of unique genes that were not changed in the other species. Regions of chromosomes harbouring inversions in one of the species tended to have more of the key genes within them, compared to other genomic regions. This was also true for species that did not have inversions in those regions. This demonstrates that genes in regions affected by chromosome inversions can still help plants adapt to changes in the environment even in the absence of inversions. Sunflowers are widely grown for their edible oily seeds. In the future, some of the key genes identified in this work may be useful candidates for plant breeding to improve the resilience of sunflowers to drought, high temperatures and other environmental challenges.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.097 | 0.016 |
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".