Toward the development of a molecular toolkit for the microbial remediation of per-and polyfluoroalkyl substances
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) are highly fluorinated synthetic organic compounds that have been used extensively in various industries owing to their unique properties. The PFAS family encompasses diverse classes, with only a fraction being commercially relevant. These substances are found in the environment, including in water sources, soil, and wildlife, leading to human exposure and fueling concerns about potential human health impacts. Although PFAS degradation is challenging, biodegradation offers a promising, eco-friendly solution. Biodegradation has been effective for a variety of organic contaminants but is yet to be successful for PFAS due to a paucity of identified microbial species capable of transforming these compounds. Recent studies have investigated PFAS biotransformation and fluoride release; however, the number of specific microorganisms and enzymes with demonstrable activity with PFAS remains limited. This review discusses enzymes that could be used in PFAS metabolism, including haloacid dehalogenases, reductive dehalogenases, cytochromes P450, alkane and butane monooxygenases, peroxidases, laccases, desulfonases, and the mechanisms of microbial resistance to intracellular fluoride. Finally, we emphasize the potential of enzyme and microbial engineering to advance PFAS degradation strategies and provide insights for future research in this field.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".