A Novel Role for an ECF Sigma Factor in Fatty Acid Biosynthesis and Membrane Fluidity in Pseudomonas aeruginosa
Bibliographic record
Abstract
Extracytoplasmic function (ECF) sigma factors are members of cell-surface signaling systems, abundant in the opportunistic pathogen Pseudomonas aeruginosa. Twenty genes coding for ECF sigma factors are present in P. aeruginosa sequenced genomes, most of them being part of TonB systems related to iron uptake. In this work, poorly characterized sigma factors were overexpressed in strain PA14, in an attempt to understand their role in the bacterium's physiology. Cultures overexpressing SigX displayed a biphasic growth curve, reaching stationary phase earlier than the control strain, followed by subsequent growth resumption. During the first stationary phase, most cells swell and die, but the remaining cells return to the wild type morphology and proceed to a second exponential growth. This is not due to compensatory mutations, since cells recovered from late time points and diluted into fresh medium repeated this behavior. Swollen cells have a more fluid membrane and contain higher amounts of shorter chain fatty acids. A proteomic analysis was performed to identify differentially expressed proteins due to overexpression of sigX, revealing the induction of several fatty acid synthesis (FAS) enzymes. Using qRT-PCR, we showed that at least one isoform from each of the FAS pathway enzymes were upregulated at the mRNA level in the SigX overexpressing strain thus pointing to a role for this ECF sigma factor in the FAS regulation in P. aeruginosa.
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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) | 0.000 | 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".