Habitat-specific type I polyketide synthases in soils and street sediments
Bibliographic record
Abstract
Actinomycetes produce many pharmaceutically useful compounds through type I polyketide biosynthetic pathways. Soil has traditionally been an important source for these actinomycete-derived pharmaceuticals. As the rate of antibiotic discovery has decreased and the incidence of antibiotic resistance has increased, researchers have looked for alternatives to soil for bioprospecting. Street sediment, where actinomycetes make up a larger fraction of the bacterial population than in soil, is one such alternative environment. To determine if these differences in actinomycetal community structure are reflected in type I polyketide synthases (PKSI) distribution, environmental DNA from soils and street sediments was characterized by sequencing amplicons of PKSI-specific PCR primers. Amplicons covered two domains: the last 80 amino acids of the ketosynthase (KS) domain and the first 240 amino acids of the acyltransferase (AT) domain. One hundred and ninety clones from ten contrasting soils from six regions and nine street sediments from six cities were sequenced. Twenty-five clones from two earthworm-affected samples were also sequenced. UniFrac lineage-specific analysis identified two clades that clustered with actinomycetal GenBank matches that were street sediment-specific, one similar to the PKSI segment of the mycobactin siderophore involved in mycobacterial virulence. A clade of soil-specific sequences clustered with GenBank matches from the ambruticin and jerangolid pathways of Sorangium cellulosum. All three of these clades were found in sites >700 km apart. Street sediments are enriched in actinomycetal PKSIs. Non-actinomycetal PKSI pathways may be more chemically diverse than actinomycetal PKSIs. Common soil and street sediment PKIs are globally distributed.
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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 | 0.000 |
| 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 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".