Bibliographic record
Abstract
In syntrophic interactions, metabolic pathways are integrated over different cell types. This chapter focuses on two seemingly well-defined groups of secondary metabolites, quorum-sensing signals and antibiotics, to demonstrate that their described biological activities do not necessarily define their functional roles in microbial communities. The study of the biology of living organisms and associated biochemical processes has, to date, focused primarily on the structures and functions of DNA, RNA, proteins, lipids, carbohydrates, and their macromolecular complexes. Many bacteria regulate gene expression in response to accumulation of secondary metabolites, and this behavior has been collectively referred to as quorum sensing or cell-cell communication. The generalization of quorum sensing as a density-dependent process ignores the reality that most bacteria do not exist in well-stirred reactors and the signaling will largely be a local event between small groups of cells. The dual role of quorum-sensing signal and antibiotic is not exclusive to nisin, subtilin, and mercascidin peptide antibiotics. A bactericidal activity produced by a strain of Rhizobium leguminosarum that inhibited the growth of several related strains was purified and demonstrated to be a typical acyl homoserine lactone (AHL) [N-(3-hydroxy-7-cis-tetradecenoyl)-L-homoserine lactone]. The streptomycin and chloramphenicol resistance determinants were later shown to catalyze chemical inactivation of the corresponding antibiotic. In addition to the widespread occurrence of antibiotic resistance mechanisms, it has become apparent in recent years that there are many naturally occurring systems that interfere with cell-cell signaling pathways.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".