Enzyme Catalyzed Formation of CoA Adducts of Fluorinated Hexanoic Acid Analogues using a Long-Chain acyl-CoA Synthetase from <i>Gordonia</i> sp. Strain NB4-1Y
Bibliographic record
Abstract
Per and polyfluoroalkyl substances (PFAS) are a large family of anthropogenic fluorinated chemicals of increasing environmental concern. Over recent years, numerous microbial communities have been found to be capable of metabolizing some polyfluoroalkyl substances, generating a range of low-molecular-weight PFAS metabolites. One proposed pathway for the microbial breakdown of fluorinated carboxylates includes β-oxidation, this pathway is initiated by the formation of a CoA adduct. However, until recently no PFAS-CoA adducts had been reported. In a previous study, we were able to use a bacterial medium-chain acyl-CoA synthetase (mACS) to form CoA adducts of fluorinated adducts of propanoic acid and pentanoic acid but were not able to detect any products of fluorinated hexanoic acid analogues. Herein, we expressed and purified a long-chain acyl-CoA synthetase (lACS) and a A461K variant of mACS from the soil bacterium Gordonia sp. strain NB4–1Y and performed an analysis of substrate scope and enzyme kinetics using fluorinated and nonfluorinated carboxylates. We determined that lACS can catalyze the formation of CoA adducts of 1:5 fluorotelomer carboxylic acid (FTCA), 2:4 FTCA and 3:3 FTCA, albeit with generally low turnover rates (<0.02 s –1 ) compared with the nonfluorinated hexanoic acid (5.39 s –1 ). In addition, the A461K variant was found to have an 8-fold increase in selectivity toward hexanoic acid compared with wild-type mACS, suggesting that Ala-461 has a mechanistic role in selectivity toward substrate chain length. This provides further evidence to validate the proposed activation step involving the formation of CoA adducts in the enzymatic breakdown of PFAS.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".