Experimental characterization of the mechanism of perfluorocarboxylic acids' liver protein bioaccumulation: The key role of the neutral species
Bibliographic record
Abstract
Perfluorocarboxylic acids (PFCAs) of chain length greater than seven carbon atoms bioconcentrate in the livers of fish. However, a mechanistic cause for the empirically observed increase in the bioconcentration potential of PFCAs as a function of chain length has yet to be determined. To this end, recombinant rat liver fatty acid-binding protein (L-FABP) was purified, and its interaction with PFCAs was characterized in an aqueous system at pH 7.4. Relative binding affinities of L-FABP with PFCAs of carbon chain lengths of five to nine were established fluorimetrically. The energetics, mechanism, and stoichiometry of the interaction of perfluorooctanoic acid (PFOA) with L-FABP were examined further by isothermal titration calorimetry (ITC) and electrospray ionization combined with tandem mass spectrometry (ESI-MS/MS). Perfluorooctanoic acid was shown to bind to L-FABP with an affinity approximately an order of magnitude less than the natural ligand, oleic acid, and to have at least 3:1 PFOA:L-FABP stoichiometry. Two distinct modes of PFOA binding to L-FABP were observed by ESI-MS/MS analysis; in both cases, PFOA binds solely as the neutral species under typical physiological pH and aqueous concentrations of the anion. A comparison of their chemical and physical properties with other well-studied biologically relevant chemicals showed that accumulation of PFCAs in proteins as the neutral species is predictable. For example, the interaction of PFOA with L-FABP is almost identical to that of the acidic ionizing drugs ketolac, ibuprofen, and warfarin that show specificity to protein partitioning with a magnitude that is proportional to the K(OW) (octanol-water partitioning) of the neutral species. The experimental results suggest that routine pharmacochemical models may be applicable to predicting the protein-based bioaccumulation of long-chain PFCAs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".