Identification of Arsenic-Binding Proteins in Human Cells by Affinity Chromatography and Mass Spectrometry
Bibliographic record
Abstract
Exposure to high levels of arsenic can cause a wide range of health effects, including cancers of the bladder, lung, skin, and kidney. However, the mechanism(s) of action underlying these deleterious effects of arsenic remains unclear. Arsenic binding to cellular proteins is a possible mechanism of toxicity, and identifying such binding is analytically challenging because of the large concentration range and variety of proteins. We describe here an affinity selection technique, coupled with mass spectrometry, to select and identify specific arsenic-binding proteins from a large pool of cellular proteins. Controlled experiments using proteins either containing free cysteine(s) or having cysteine blocked showed that the arsenic affinity column specifically captured the proteins containing free cysteine(s) available to bind to arsenic. The technique was able to capture and identify trace amounts of bovine biliverdin reductase B present as a minor impurity in the commercial preparation of carbonic anhydrase II, demonstrating the ability to identify arsenic-binding proteins in the presence of a large excess of non-specific proteins. Application of the technique to the analysis of subcellular fractions of A549 human lung carcinoma cells identified 50 proteins in the nuclear fraction, and 24 proteins in the membrane/organelle fraction that could bind to arsenic, adding to the current list of only a few known arsenic-binding proteins.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".