The Impact of Heterologous Regulatory Genes from Lipodepsipeptide Biosynthetic Gene Clusters on the Production of Teicoplanin and A40926
Bibliographic record
Abstract
StrR-like pathway-specific transcriptional regulators (PSRs) function as activators in the biosynthesis of various antibiotics, including glycopeptides (GPAs), aminoglycosides, aminocoumarins, and ramoplanin-like lipodepsipeptides (LDPs). In particular, the roles of StrR-like PSRs have been previously investigated in the biosynthesis of streptomycin, novobiocin, GPAs like balhimycin, teicoplanin, and A40926, as well as LDP enduracidin. In the current study, we focused on StrR-like PSRs from the ramoplanin biosynthetic gene cluster (BGC) in Actinoplanes ramoplaninifer ATCC 33076 (Ramo5) and the chersinamycin BGC in Micromonospora chersina DSM 44151 (Chers28). Through the analysis of the amino acid sequences of Ramo5 and Chers28, we discovered that these proteins are phylogenetically distant from other experimentally investigated StrR PSRs, although all StrR-like PSRs found in BGCs for different antibiotics share a conserved secondary structure. To investigate whether Ramo5 and Chers28, given their phylogenetic positions, might influence the biosynthesis of other antibiotic pathways governed by StrR-like PSRs, the corresponding genes (ramo5 and chers28) were heterologously expressed in Actinoplanes teichomyceticus NRRL B-16726 and Nonomuraea gerenzanensis ATCC 39727, which produce the clinically-relevant GPAs teicoplanin and A40926, respectively. Recombinant strains of NRRL B-16726 and ATCC 39727 expressing chers28 exhibited improved antibiotic production, although the expression of ramo5 did not yield the same effect. These results demonstrate that some StrR-like PSRs can “cross-talk” between distant biosynthetic pathways and might be utilized as tools for the activation of silent BGCs regulated by StrR-like PSRs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".