Elevated Carbon Dioxide Levels Decreases Cucumber Mosaic Virus Accumulation in Correlation with Greater Accumulation of rgs-CaM, an Inhibitor of a Viral Suppressor of RNAi
Bibliographic record
Abstract
Plant viruses cause a range of plant diseases symptoms that are often responsible for significant crop production losses and the severity and spread of the symptoms may be affected by climate change. While the increase in anthropogenic activities has caused a critical problem of increased CO2 levels in the atmosphere, these elevated CO2 levels have been reported to reduce virus disease severity in some plant species. In such instances, it is not clear if the plant defense mechanisms are being enhanced or virus-mediated mechanisms to overcome plant resistance are being defeated. Additionally, a few studies have been attempted in this area to determine if reduced disease is the norm or the exception under enhanced CO2 levels. In the present study, the effects of elevated CO2 levels (750 ppm vs. 390 ppm) on RNAi-mediated resistance of Nicotiana tabacum against the cucumber mosaic virus (CMV), and the activity of viral suppressor of RNAi (VSR) 2b protein of CMV were evaluated. On the one hand, our results showed that elevated CO2 decreased the transcription of dicer-like protein 2 (DCL2), DCL4, and argonaut 1 (AGO1) genes with functions related to RNAi-mediated resistance when infected by CMV, which is contradictory with the decreased CMV copy numbers under elevated CO2. On the other hand, we found that elevated CO2 increased the calcium concentration and expression of the calcium-binding protein rgs-CaM in tobacco plants when infected by CMV, which directly weakened the function of 2b protein, the VSR of CMV, and therefore decreased the infection efficiency of the virus and suppressed the severity of CMV in tobacco plants under elevated CO2. This study provides molecular insights into the ecological implications underlying the development of prevention strategies against plant virus infection in the context of climate change.
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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".