Haloacetonitriles Induce Structure-Related Cellular Toxicity Through Distinct Proteome Thiol Reaction Mechanisms
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Haloacetonitriles (HANs) are a class of toxic drinking water disinfection byproducts (DBPs). However, the toxicity mechanisms of HANs remain unclear. We herein investigated the structure-related in vitro toxicity of 6 representative HANs by utilizing complementary bioanalytical approaches. Dibromoacetonitrile (DBAN) displayed strong cytotoxicity and Nrf2 oxidative stress responses, followed by monohalogenated HANs (monoHANs) while other polyhalogenated HANs (polyHANs) exhibited little toxicity. Activity based protein profiling (ABPP) revealed that toxic HANs adduct to human proteome thiols, supporting thiol reactivity as the primary toxicity mechanism for HANs. By using glutathione (GSH) as a thiol surrogate, monoHANs reacted with GSH via S N 2 while polyHANs reacted through ultrafast addition reactions. In contrast, DBAN generated an unexpected fully debrominated product and glutathione disulfide (GSSG). The unique reaction of DBAN with GSH was found to be mediated by radicals which was supported by electron paramagnetic resonance (EPR) spectroscopy and by radical trapping reagent reaction quenching. Shotgun proteomics further revealed that monoHANs and DBAN adducted to proteome thiols in live cells forming dehalogenated adducts. Multiple antioxidant proteins, SOD1, CSTB, and GAPDH, were adducted by toxic HANs at specific cysteine residues. This study highlights the structurally selective toxicity of HANs in human cells, which are attributed to their distinct reactions with proteome thiols.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".