Maintaining Symbiotic Homeostasis: How Do Plants Engage With Beneficial Microorganisms While at the Same Time Restricting Pathogens?
Bibliographic record
Abstract
This article is part of the Top 10 Unanswered Questions in MPMI invited review series.That plants recruit beneficial microbes while simultaneously restricting pathogens is critical to their survival. Plants must exclude pathogens; however, most land plants are able to form mutualistic symbioses with arbuscular mycorrhizal fungi. Plants also associate with the complex microbial communities that form the microbiome. The outcome of each symbiotic interaction-whether a specific microbe is pathogenic, commensal, or mutualistic-relies on the specific interplay of host and microbial genetics and the environment. Here, we discuss how plants use metabolites as a gate to select which microbes can be symbiotic. Once present, we discuss how plants integrate multiple inputs to initiate programs of immunity or mutualistic symbiosis and how this paradigm may be expanded to the microbiome. Finally, we discuss how environmental signals are integrated with immunity to fine-tune a thermostat that determines whether a plant engages in mutualism, resistance to pathogens, and shapes associations with the microbiome. Collectively, we propose that the plant immune thermostat is set to select for and tolerate a largely nonharmful microbiome while receptor-mediated decision making allows plants to detect and dynamically respond to the presence of potential pathogens or mutualists.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".