Complex Interplay of Mercury and Arsenic with Sulfur and Selenium in Biological Systems
Bibliographic record
Abstract
Mercury and arsenic compounds are hazardous, known for their extreme toxicity among heavy metals and metalloids, and prevalent in the environment with potential for human exposure. The magnitude and severity of the health issues they cause endanger the lives exposed to them. Therefore, a detailed molecular understanding of the complex interplay of arsenic and mercury with sulfur and selenium is crucial to discerning the mechanism behind the toxicity of arsenite and mercury compounds. While certain mercury and arsenic species are known to inhibit selenoenzymes, namely thioredoxin reductases (TrxRs), the molecular level interactions remain unexplored. To gain deeper insights into such interactions, this dissertation employed synchrotron X-ray absorption spectroscopy (XAS) alongside computational chemistry techniques to characterize the chemical coordination of arsenic and mercury in sulfur- and selenium-containing systems relevant to their toxicity. High-energy resolution fluorescence detected XAS (HERFD-XAS) and computational studies informed on the formation of various mercury-thiolate complexes, laying the groundwork for a deeper understanding of the toxic effects of mercury. Furthermore, the analysis of mercury-thiol interactions prompted additional studies of the oxidation process of the thiols themselves. To achieve this, a stopped-flow reaction system was introduced which can be used to examine a wide range of relevant thiol-containing reaction processes. The initial results from this system showcase the complexity of the oxidation of glutathione with hydrogen peroxide. Extended X-ray absorption fine structure (EXAFS) together with density functional theory (DFT) studies of TrxR1 interacting with methylmercury and inorganic mercury provided detailed structural characterization of the modes of mercury coordination in selenoenzymes, informing on toxic effects. The same approach was instrumental in investigating TrxR1 binding to arsenite, the active compound of the chemotherapy drug Trisenox. The binding of mercury and arsenic to selenium from TrxR1 highlights the susceptibility of selenium as a target for these toxic compounds. This dissertation presents compelling evidence on the involvement of biological thiols and TrxR1 in mercury toxicity, while also identifying TrxR1 as targets for arsenite. Overall, this research lays the foundation for a deeper understanding of mercury toxicity and the development of future anticancer drugs based on less toxic alternatives to arsenite, focusing on TrxRs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".