The Global Anaerobic Regulator Anr, Is Involved in Cell Attachment and Aggregation Influencing the First Stages of Biofilm Development in Pseudomonas extremaustralis
Bibliographic record
Abstract
Pseudomonas extremaustralis is a versatile Antarctic bacterium, able to grow under microaerobic and anaerobic conditions and is related to several non-pathogenic Pseudomonads. Here we report on the role of the global anaerobic regulator Anr, in the early steps of P. extremaustralis biofilm development. We found that the anr mutant was reduced in its ability to attach, to form aggregates and to display twitching motility but presented higher swimming motility than the wild type. In addition, microscopy revealed that the wild type biofilm contained more biomass and was thicker, but were less rough than that of the anr mutant. In silico analysis of the P. extremaustralis genome for Anr-like binding sites led to the identification of two biofilm-related genes as potential targets of this regulator. When measured using Quantitative Real Time PCR, we found that the anr mutant expressed lower levels of pilG, which encodes a component of Type IV pili and has been previously implicated in cellular adhesion. Levels of morA, involved in signal transduction and flagella development, were also lower in the mutant. Our data suggest that under low oxygen conditions, such as those encountered in biofilms, Anr differentially regulates aggregation and motility thus affecting the first stages of biofilm formation.
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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 | 0.000 |
| 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".