The wonderful world of intrinsic and intricate immunity responses in plants against pathogens
Bibliographic record
Abstract
Plants, unlike animals, lack specialized mobile immune cells, so they do not have an adaptive immune system. Instead, plants can launch specific, self-tolerant immune responses and establish immune memory. Plants possess defense mechanisms that efficiently detect and ward off potentially dangerous microorganisms. These defense mechanisms start with multiple signalling processes responsible for sensation, recognition, signal collection and conveying information between cells. Recognition occurs when microbial or pathogen-associated molecular patterns (MAMPs or PAMPs) are detected, leading to MAMP- or PAMP-triggered immunity (MTI or PTI). Plant cells also recognize pathogens through effector-triggered immunity (ETI), which relies on the function of the pathogen’s avirulence (Avr) gene-coded effector proteins and host’s resistance (R) gene-coded R proteins, resulting in the activation of apoptosis-like cell death, known as the hypersensitive response. In addition, reactive oxygen species (ROS) act as a double-edged sword; they are either toxic or versatile signalling molecules in plants. ROS generation is an integral part of hormone regulation and function in plant defense mechanisms. Plant hormones are also implicated in plant defense signalling pathways; salicylic acid, jasmonic acid, and ethylene have been increasingly studied in plant responses to pathogens. These innate immune system components interact with each other and provide protection against invading pathogens. We review advances in understanding the molecular aspects of plant defense mechanisms and describe the role of ROS, mitogen-activated protein kinase (MAPK) cascades, and hormones in modulating defense responses. We also provide an overview of how these plant defense components interact for a balanced and appropriate defense response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".