Anaerobic fluorescent reporters for live imaging of Pseudomonas aeruginosa
Bibliographic record
Abstract
Pseudomonas aeruginosa thrives in the airways of individuals with cystic fibrosis, in part by forming robust biofilms that are resistant to immune clearance or antibiotic treatment. In the cystic fibrosis lung, the thickened mucus layers create an oxygen gradient, often culminating with the formation of anoxic pockets. In this environment, P. aeruginosa can use nitrate instead of oxygen to grow. Current fluorescent reporters for studying P. aeruginosa are limited to the GFP and related analogs. However, these reporters require oxygen for the maturation of their chromophore, making them unsuitable for the study of anaerobically grown P. aeruginosa. To overcome this limitation, we evaluated seven alternative fluorescent proteins, including iLOV, phiLOV2.1, evoglow-Bs2, LucY, UnaG, Fluorescence-Activating and Absorption-Shifting Tag (FAST), and iRFP670, which have been reported to emit light under oxygen-limiting conditions. We generated a series of plasmids encoding these proteins and validated their fluorescence using plate reader assays and confocal microscopy. Six of these proteins successfully labeled P. aeruginosa in anoxia. In particular, phiLOV2.1 and FAST provided superior fluorescence stability and enabled dual-color imaging of both planktonic and biofilm cultures. This study provides a set of fluorescent reporters for monitoring P. aeruginosa under low-oxygen conditions. These reporters will facilitate studies of P. aeruginosa in biofilms or other contexts relevant to its pathogenesis, such as those found in cystic fibrosis airways. Due to the broad host range of our expression vector, the phiLOV2.1 and FAST-based reporters may be applicable to the study of other Gram-negative bacteria that inhabit similar low-oxygen niches.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".