Knockout of Extracytoplasmic Function Sigma Factor ECF-10 Affects Stress Resistance and Biofilm Formation in Pseudomonas putida KT2440
Bibliographic record
Abstract
Pseudomonas putida is a Gram-negative soil bacterium which is well-known for its versatile lifestyle, controlled by a large repertoire of transcriptional regulators. Besides one- and two-component regulatory systems, the genome of P. putida reveals 19 extracytoplasmic function (ECF) sigma factors involved in the adaptation to changing environmental conditions. In this study, we demonstrate that knockout of extracytoplasmic function sigma factor ECF-10, encoded by open reading frame PP4553, resulted in 2- to 4-fold increased antibiotic resistance to quinolone, β-lactam, sulfonamide, and chloramphenicol antibiotics. In addition, the ECF-10 mutant exhibited enhanced formation of biofilms after 24 h of incubation. Transcriptome analysis using Illumina sequencing technology resulted in the detection of 12 genes differentially expressed (>2-fold) in the ECF-10 knockout mutant strain compared to their levels of expression in wild-type cells. Among the upregulated genes were ttgA, ttgB, and ttgC, which code for the major multidrug efflux pump TtgABC in P. putida KT2440. Investigation of an ECF-10 and ttgA double-knockout strain and a ttgABC-overexpressing strain demonstrated the involvement of efflux pump TtgABC in the stress resistance and biofilm formation phenotypes of the ECF-10 mutant strain, indicating a new role for this efflux pump beyond simple antibiotic resistance in P. putida KT2440.
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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) | 0.000 | 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".