Exploring the Diversity of Mechanisms Associated With Plant Tolerance to Virus Infection
Bibliographic record
Abstract
Tolerance is defined as an interaction in which viruses accumulate to some degree without causing significant loss of vigor or fitness to their hosts. Tolerance can be described as a stable equilibrium between the virus and its host, an interaction in which each partner not only accommodate trade-offs for survival but also receive some benefits (e.g., protection of the plant against super-infection by virulent viruses; virus invasion of meristem tissues allowing vertical transmission). This equilibrium, which would be associated with little selective pressure for the emergence of severe viral strains, is common in wild ecosystems and has important implications for the management of viral diseases in the field. Plant viruses are obligatory intracellular parasites that divert the host cellular machinery to complete their infection cycle. Highjacking/modification of plant factors can affect plant vigor and fitness. In addition, the toxic effects of viral proteins and the deployment of plant defense responses contribute to the induction of symptoms ranging in severity from tissue discoloration to malformation or tissue necrosis. The impact of viral infection is also influenced by the virulence of the specific virus strain (or strains for mixed infections), the host genotype and environmental conditions. Although plant resistance mechanisms that restrict virus accumulation or movement have received much attention, molecular mechanisms associated with tolerance are less well-understood. We review the experimental evidence that supports the concept that tolerance can be achieved by reaching the proper balance between plant defense responses and virus counter-defenses. We also discuss plant translation repression mechanisms, plant protein degradation or modification pathways and viral self-attenuation strategies that regulate the accumulation or activity of viral proteins to mitigate their impact on the host. Finally, we discuss current progress and future opportunities toward the application of various tolerance mechanisms in the field.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".