Identification of genes that contribute to drought tolerance in Populus
Bibliographic record
Abstract
Populus species and derived hybrids are valued for their fast growth and are cultivated all over the Northern hemisphere.They are grown primarily for pulp, paper and oriented strand board production.Fast growing poplar also has potential to be used for carbon sequestration as well as a feedstock for the carbon-neutral production of energy.Many of the commonly used species and hybrids are, however, regarded as drought sensitive, which poses a problem for large-scale cultivation, particularly in light of climate changeinduced drought spells in areas of poplar growth including the Canadian prairies.To evaluate the extent of drought tolerance variation in commercially important Canadian poplar hybrids, we tested their ability to withstand drought and ranked them based on a series of physiological and morphological responses.Gene expression analysis of the response to drought in the least and most tolerant clones revealed differences in abscisic acid-mediated signaling, in particular a putative negative and a putative positive regulator of this pathway.Thereafter, we tested the functional importance of these two genes by transformation experiments.Overexpression of the putative negative regulator led to reduced drought tolerance in transgenic Arabidopsis thaliana, whereas overexpression of the putative positive regulator led to improved drought tolerance in transgenic Arabidopsis thaliana and transgenic poplars.Taken together, we have generated a better understanding of drought tolerance in available fast-growing poplar hybrids, functionally characterized two poplar genes, and identified strong candidate genes for targeted improvement of drought tolerance in poplar hybrids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".