Identification of hydrocarbon sulfonates as previously overlooked transthyretin ligands in the environment
Bibliographic record
Abstract
Incidences of thyroid disease, which has long been hypothesized to be partially caused by exposure to thyroid hormone disrupting chemicals (TDCs), have rapidly increased in recent years. However, only ~1% of the binding activity of human transthyretin (hTTR), an important thyroid hormone transporter protein, can be explained by known TDCs. In this study, we aimed to identify the major hTTR ligands in Canadian indoor dust and sewage sludge by employing protein-guided nontargeted analysis. hTTR binding activities were detected in all 11 indoor dust and 9 out of 10 sewage sludge samples (median 458 and 1134 μg T4/g in dust and sludge, respectively) by the FITC-T4 displacement assay. Through employing protein Affinity Purification with Nontargeted Analysis (APNA), 31 putative hTTR ligands were detected including perfluorooctane sulfonate (PFOS). Two of the most abundant ligands were identified as hydrocarbon surfactants (e.g., dodecyl benzenesulfonate), which were confirmed by authentic chemical standards. Structure-activity relationships (SAR) of hydrocarbon surfactants were explored by investigating the binding activity of 11 hydrocarbon surfactants to hTTR. Optimal carbon chain length (C12-14) was found to achieve a high binding affinity. By employing de novo nontargeted analysis, another abundant ligand was surprisingly identified as a di-sulfonate fluorescent brightener, 4,4'-Bis(2-sulfostyryl)biphenyl sodium (CBS). CBS was validated as a nM-affinity hTTR ligand with an IC50 of 345 nM. In total, hydrocarbon surfactants and fluorescent brightener could explain 1.92-17.0% and 5.74-54.3% of hTTR binding activities in dust and sludge samples, respectively, whereas PFOS only contributed <0.0001% to the activity. Our study revealed for the first time that hydrocarbon sulfonates are previously overlooked hTTR ligands in the environment.
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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".