The complex resistomes of Paenibacillaceae reflect diverse antibiotic chemical ecologies
Bibliographic record
Abstract
The ecology of antibiotic resistance involves the interplay of a long natural history of antibiotic production in the environment, and the modern selection of resistance in pathogens through human use of these drugs. Important components of the resistome are intrinsic resistance genes of environmental bacteria, evolved and acquired over millennia, and their mobilization, which drives dissemination in pathogens. Understanding the dynamics and evolution of resistance across bacterial taxa is essential to address the current crisis in drug-resistant infections. Here we report the exploration of antibiotic resistance in the Paenibacillaceae prompted by our discovery of an ancient intrinsic resistome in Paenibacillus sp. LC231, recovered from the isolated Lechuguilla cave environment. Using biochemical and gene expression analysis, we have mined the resistome of the second member of the Paenibacillaceae family, Brevibacillus brevis VM4, which produces several antimicrobial secondary metabolites. Using phylogenomics, we show that Paenibacillaceae resistomes are in flux, evolve mostly independent of secondary metabolite biosynthetic diversity, and are characterized by cryptic, redundant, pseudoparalogous, and orthologous genes. We find that in contrast to pathogens, mobile genetic elements are not significantly responsible for resistome remodeling. This offers divergent modes of resistome development in pathogens and environmental bacteria.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".