Biodiversity of genes encoding anti-microbial traits within plant associated microbes
Bibliographic record
Abstract
The plant is an attractive versatile home for diverse associated microbes. A subset of these microbes produces a diversity of anti-microbial natural products including polyketides, non-ribosomal peptides, terpenoids, heterocylic nitrogenous compounds, volatile compounds, bacteriocins, and lytic enzymes. In recent years, detailed molecular analysis has led to a better understanding of the underlying genetic mechanisms. New genomic and bioinformatic tools have permitted comparisons of orthologous genes between species, leading to predictions of the associated evolutionary mechanisms responsible for diversification at the genetic and corresponding biochemical levels. The purpose of this review is to describe the biodiversity of biosynthetic genes of plant-associated bacteria and fungi that encode selected examples of antimicrobial natural products. For each compound, the target pathogen and biochemical mode of action are described, in order to draw attention to the complexity of these phenomena. We review recent information of the underlying molecular diversity and draw lessons through comparative genomic analysis of the orthologous coding sequences (CDS). We conclude by discussing emerging themes and gaps, discuss the metabolic pathways in the context of the phylogeny and ecology of their microbial hosts, and discuss potential evolutionary mechanisms that led to the diversification of biosynthetic gene clusters.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".