Bibliographic record
Abstract
The Streptomyces bacteria produce natural products that have potent biological activity against other organisms: 60% of our antibiotics are derived from this source. Genome sequencing reveals genes for many more. One view in the field is that these “cryptic” metabolites could serve as badly needed antibiotics for antibiotic resistant infections. However, many of them are produced at too low yields for structural and mechanistic characterization. The top priority is finding broadly applicable approaches to enhancing the yields of these molecules. To address this, I took advantage of a conserved regulator of specialized metabolism to develop a generally applicable tool, called AfsQ1*, that heterologously induces many specialized metabolic genes. Using this technology, I developed two antibiotic discovery screens. First, I identified afsQ1*-induced antibacterial activities and purified the active agents. This led to the discovery of the antibiotic siamycin-I, a potent inhibitor of antibiotic resistant Gram-Positive bacteria. I demonstrated that this compound targets the lipid-II component of cell wall biogenesis, the first of this class of molecules to do so. The second approach used comparative metabolomics to identify afsQ1*-induced novel masses. By NMR I identified a new pepticinnamin analogue, whose family are inhibitors of an eukaryotic post-translational modification called farnesylation. Farnesyl transferase inhibitors were previously investigated (unsuccessfully) as anticancer medicines. I demonstrate, however, that they are candidates for antifungal therapy, because they block morphological switching- a key virulence trait in lower fungi. This work suggests a new paradigm in antifungal therapy. Finally, I dissect an interaction between Lactobacillus reuteri and C. albicans. When co-cultured together C. albicans cannot undergo filamentous growth and I found that this inhibition is molecule-mediated. 1-acetyl β-carboline is the active metabolite isolated from L. reuteri and I found that its production is prevalent throughout the Lactobacillus genera. I also synthesized a new β-carboline analogue for future clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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".