Differential protein expression during growth on model and commercial mixtures of naphthenic acids in <i>Pseudomonas fluorescens</i> Pf‐5
Bibliographic record
Abstract
) and are among the most toxic, persistent constituents of oil sands process-affected waters (OSPW), produced during oil sands extraction. Currently, the proteins and mechanisms involved in NA biodegradation are unknown. Using LC-MS/MS shotgun proteomics, we identified proteins overexpressed during the growth of Pseudomonas fluorescens Pf-5 on a model NA (4'-n-butylphenyl)-4-butanoic acid (n-BPBA) and commercial NA mixture (Acros). By day 11, >95% of n-BPBA was degraded. With Acros, a 17% reduction in intensity occurred with 10-18 carbon compounds of the Z family -2 to -14 (major NA species in this mixture). A total of 554 proteins (n-BPBA) and 631 proteins (Acros) were overexpressed during growth on NAs, including several transporters (e.g., ABC transporters), suggesting a cellular protective response from NA toxicity. Several proteins associated with fatty acid, lipid, and amino acid metabolism were also overexpressed, including acyl-CoA dehydrogenase and acyl-CoA thioesterase II, which catalyze part of the fatty acid beta-oxidation pathway. Indeed, multiple enzymes involved in the fatty acid oxidation pathway were upregulated. Given the presumed structural similarity between alkyl-carboxylic acid side chains and fatty acids, we postulate that P. fluorescens Pf-5 was using existing fatty acid catabolic pathways (among others) during NA degradation.
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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".