Organohalogens Naturally Biosynthesized in Marine Environments and Produced as Disinfection Byproducts Alter Sarco/Endoplasmic Reticulum Ca<sup>2+</sup>Dynamics
Bibliographic record
Abstract
Contemporary sources of organohalogens produced as disinfection byproducts (DBPs) are receiving considerable attention as emerging pollutants because of their abundance, persistence, and potential to structurally mimic natural organohalogens produced by bacteria that serve signaling or toxicological functions in marine environments. Here, we tested 34 organohalogens from anthropogenic and marine sources to identify compounds active toward ryanodine receptor (RyR1), known toxicological targets of non-dioxin-like polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs). [ 3 H]Ryanodine ([ 3 H]Ry) binding screening (≤2 μM) identified 10 highly active organohalogens. Further analysis indicated that 2,3-dibromoindole ( 14 ), tetrabromopyrrole ( 31 ), and 2,3,5-tribromopyrrole ( 34 ) at 10 μM were the most efficacious at enhancing [ 3 H]Ry binding. Interestingly, these congeners also inhibited microsomal sarcoplasmic/endoplasmic reticulum (SR/ER) Ca 2+ ATPase (SERCA1a). Dual SERCA1a inhibition and RyR1 activation triggered Ca 2+ efflux from microsomal vesicles with initial rates rank ordered 31 > 34 > 14 . Hexabromobipyrroles ( 25 ) enhanced [ 3 H]Ry binding moderately with strong SERCA1a inhibition, whereas pyrrole ( 24 ), 2,3,4-tribromopyrrole ( 26 ), and ethyl-4-bromopyrrole-2-carboxylate ( 27 ) were inactive. Of three PBDE derivatives of marine origin active in the [ 3 H]Ry assay, 4′-hydroxy-2,3′,4,5′,6-pentabromodiphenyl ether ( 18 ) was also a highly potent SERCA1a inhibitor. Molecular targets of marine organohalogens that are also DBPs of emerging environmental concern are likely to contribute to their toxicity.
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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) | 0.001 | 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".