GGR (Geranylgeranyl Reductase) Expression Affects the Allelopathic Response to Arabidopsis Allelochemicals
Bibliographic record
Abstract
Allelopathy involves the release of compounds into the environment that affects the growth and development of other organisms. This phenomenon may lead to the production of compounds less harmful to the environment than traditional herbicides used in weed control. In plants, terpenes have been identified as components of allelochemicals and are synthesized by enzymes named as geranylgeranyl diphosphate synthases (GGPPS). There are about 12 GGPPS genes in Arabidopsis, among which is GGR. This work aims to study the association between the expression levels of GGR and the allelopathic response of sesame seedlings to Arabidopsis leaf extracts. Hence, the GGR gene was inserted into Arabidopsis with the purpose to investigate the allelopathic effects of GGR expression levels on sesame seedlings. GGR expression levels were quantified by RT-PCR in both transgenic and non-transgenic [wild-type (WT)] lines. It has been observed that both wild-type and GGR expressing transgenic lines inhibited the growth of sesame seedlings. However, it is noteworthy that the phytotoxicity of extracts from GGR lines were greater than WT extracts. RT-PCR analysis of GGR expression revealed that WT plants had higher levels of GGR expression than GGR transgenic lines, which suggests that a homologous-dependent gene silencing (HDGS) occurred in GGR lines. GGR is part of an enzyme complex that works as a hub that determines the types of terpenes produced in Arabidopsis chloroplasts. The present data indicates that decreases in GGR expression may have favoured the production of terpenes with stronger allelopathic capacity in Arabidopsis leaves.
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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 | 0.000 | 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 | 0.001 |
| 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".