Genome-wide expression analysis offers new insights into the origin and evolution of Physcomitrella patens stress response
Bibliographic record
Abstract
Changes in the environment, such as those caused by climate change, can exert stress on plant growth, diversity and ultimately global food security. Thus, focused efforts to fully understand plant response to stress are urgently needed in order to develop strategies to cope with the effects of climate change. Because Physcomitrella patens holds a key evolutionary position bridging the gap between green algae and higher plants, and because it exhibits a well-developed stress tolerance, it is an excellent model for such exploration. Here, we have used Physcomitrella patens to study genome-wide responses to abiotic stress through transcriptomic analysis by a high-throughput sequencing platform. We report a comprehensive analysis of transcriptome dynamics, defining profiles of elicited gene regulation responses to abiotic stress-associated hormone Abscisic Acid (ABA), cold, drought, and salt treatments. We identified more than 20,000 genes expressed under each aforementioned stress treatments, of which 9,668 display differential expression in response to stress. The comparison of Physcomitrella patens stress regulated genes with unicellular algae, vascular and flowering plants revealed genomic delineation concomitant with the evolutionary movement to land, including a general gene family complexity and loss of genes associated with different functional groups.
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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 | 0.001 |
| 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) | 0.000 | 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".