BosR: A novel biofilm-specific regulator in Pseudomonas aeruginosa
Bibliographic record
Abstract
Biofilms are the most common cause of bacterial infections in humans and notoriously hard to treat due to their ability to withstand antibiotics and host immune defenses. To overcome the current lack of effective antibiofilm therapies and guide future design, the identification of novel biofilm-specific gene targets is crucial. In this regard, transcriptional regulators have been proposed as promising targets for antimicrobial drug design. Therefore, a Transposon insertion sequencing approach was employed to systematically identify regulators phenotypically affecting biofilm growth in Pseudomonas aeruginosa PA14 using the TnSeq analysis tools Bio-TraDIS and TRANSIT. A screen of a pool of 300,000 transposon insertion mutants identified 349 genes involved in biofilm growth on hydroxyapatite, including 47 regulators. Detection of 19 regulatory genes participating in well-established biofilm pathways validated the results. An additional 28 novel prospective biofilm regulators suggested the requirement for multiple one-component transcriptional regulators. Biofilm-defective phenotypes were confirmed for five one-component transcriptional regulators and a protein kinase, which did not affect motility phenotypes. The one-component transcriptional regulator bosR displayed a conserved role in P. aeruginosa biofilm growth since its ortholog in P. aeruginosa strain PAO1 was also required for biofilm growth. Microscopic analysis of a chromosomal deletion mutant of bosR confirmed the role of this regulator in biofilm growth. Overall, our results highlighted that the gene network driving biofilm growth is complex and involves regulators beyond the primarily studied groups of two-component systems and cyclic diguanylate signaling proteins. Furthermore, biofilm-specific regulators, such as bosR, might constitute prospective new drug targets to overcome biofilm infections.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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".