The degree and position of phosphorylation determine the impact of toxic and trace metals on phosphoinositide containing model membranes
Bibliographic record
Abstract
This work assessed effects of metal binding on membrane fluidity, liposome size, and lateral organization in biomimetic membranes composed of 1 mol% of selected phosphorylated phosphoinositides in each system. Representative examples of phosphoinositide phosphate, bisphosphate and triphosphate were investigated. These include phosphatidylinositol-(4,5)-bisphosphate, an important signaling lipid constituting a minor component in plasma membranes whereas phosphatidylinositol-(4,5)-bisphosphate clusters support the propagation of secondary messengers in numerous signaling pathways. The high negative charge of phosphoinositides facilitates electrostatic interactions with metals. Lipids are increasingly identified as toxicological targets for divalent metals, which potentially alter lipid packing and domain formation. Exposure to heavy metals, such as lead and cadmium or elevated levels of essential metals, like cobalt, nickel, and manganese, implicated with various toxic effects were investigated. Phosphatidylinositol-(4)-phosphate and phosphatidylinositol-(3,4,5)-triphosphate containing membranes are rigidified by lead, cobalt, and manganese whilst cadmium and nickel enhanced fluidity of membranes containing phosphatidylinositol-(4,5)-bisphosphate. Only cobalt induced liposome aggregation. All metals enhanced lipid clustering in phosphatidylinositol-(3,4,5)-triphosphate systems, cobalt in phosphatidylinositol-(4,5)-bisphosphate systems, while all metals showed limited changes in lateral film organization in phosphatidylinositol-(4)-phosphate matrices. These observed changes are relevant from the biophysical perspective as interference with the spatiotemporal formation of intricate domains composed of important signaling lipids may contribute to metal 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".