Identification of innate immunity elicitors using molecular signatures of natural selection
Bibliographic record
Abstract
The innate immune system is an ancient and broad-spectrum defense system found in all eukaryotes. The detection of microbial elicitors results in the up-regulation of defense-related genes and the elicitation of inflammatory and apoptotic responses. These innate immune responses are the front-line barrier against disease because they collectively suppress the growth of the vast majority of invading microbes. Despite their critical role, we know remarkably little about the diversity of immune elicitors. To address this paucity, we reasoned that hosts are more likely to evolve recognition to "core" pathogen proteins under strong negative selection for the maintenance of essential cellular functions, whereas repeated exposure to host-defense responses will impose strong positive selective pressure for elicitor diversification to avoid host recognition. Therefore, we hypothesized that novel bacterial elicitors can be identified through these opposing forces of natural selection. We tested this hypothesis by examining the genomes of six bacterial phytopathogens and identifying 56 candidate elicitors that have an excess of positively selected residues in a background of strong negative selection. We show that these positively selected residues are atypically clustered, similar to patterns seen in the few well-characterized elicitors. We then validated selected candidate elicitors by showing that they induce Arabidopsis thaliana innate immunity in functional (virulence suppression) and cellular (callose deposition) assays. These finding provide targets for the study of host-pathogen interactions and applied research into alternative antimicrobial treatments.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".