Development of a rapid screen to identify formulations that enhance plant resistance to viral infection
Bibliographic record
Abstract
A screening method was developed to identify formulations that enhance plant resistance to viral infection. A modified Tobacco rattle virus (TRV) genome with green fluorescent protein (TRV-GFP) was delivered into Nicotiana benthamiana cells using Agrobacterium tumefaciens . Subjective scales based on TRV-GFP fluorescence in inoculated and systemic leaves were created and validated by examining TRV coat protein expression using RT-PCR. This rapid 7-day Agrobacterium -TRV-GFP/ N. benthamiana screen was used to test many formulations containing sodium magnesium chlorophyllin (Mg-chl) with various surfactants. Treatment with Mg-chl formulation 1 and 2 resulted in significant reductions in TRV-GFP levels in N. benthamiana inoculated and systemic leaves compared to mock-treated plants, without causing phytotoxic effects. Given that Mg-chl is activated by light to produce reactive oxygen species (ROS), H 2 O 2 levels were examined and shown to increase in Mg-chl-treated leaves. Elevated H 2 O 2 levels may have initiated Salicylic acid (SA)- and Jasmonic acid (JA)-mediated resistance pathways as supported by expression of N.benthamiana Pathogenesis-Related1 ( NbPR1 ) and N.benthamiana Myelocytomatosis transcription factor ( NbMYC2) in formulation-treated leaves. Treatment with formulations 1 and 2 also provided resistance in Nicotiana tabacum leaves to Tobacco mosaic virus (TMV). Given that the JA pathway gene NbMYC2 was expressed in response to Mg-chl treatment, resistance to necrotrophic pathogens or insects may also be enhanced. Treatment with Mg-chl formulation 1 and 2 conferred resistance to TRV-GFP in N. benthamiana and TMV in N. tabacum without causing phytotoxic effects, indicating these formulations are ideal candidates for commercialization as plant immunity stimulators.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".