The stress response of pokeweed and phylogeny of a defense gene family in plants
Bibliographic record
Abstract
Pokeweed (Phytolacca americana L.) is a non-model plant known for its resistance to a variety of biotic and abiotic stressors. Part of the reason for this is the presence of ribosome inactivating proteins (RIPs), which can hydrolyze adenine bases from nucleic acids. These proteins are upregulated in pokeweed by jasmonic acid, a plant hormone involved in stress response. The goal of this research was to gain a better understanding of how plants respond to stress and so two different, but complementary, approaches were taken. Firstly, an in-depth look at the diversity and evolution of RIPs in plants was undertaken by curating a dataset of RIPs from publicly available data and using computational approaches to characterize their domain architecture, identify conserved amino acids, and construct a gene tree. This research revealed that despite the damage that RIPs can potentially cause to the plant’s own nucleic acids, RIPs are common among plants and their diversity indicates the potential for a multi-faceted impact on plant defense. Looking more closely at how pokeweed responds to stress, we applied jasmonic acid to leaves and analyzed changes in gene expression, through RNA sequencing (RNA-Seq). Identification of gene clusters involved in defense was aided by the generation of a pokeweed genome assembly. This research revealed that there is a variety of strategies that plants can implement to respond to stress, and that these strategies are applied differently by different species. Overall, this research contributes to a better understanding of the diversity and nuance present in the ways plants defend themselves.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".