A homology-based 3D model and structure–function studies reveal key elements for divalent metal ion transporter ZIP8 (SLC39A8) function
Bibliographic record
Abstract
The divalent metal transporter ZIP8 ( SLC39A8 ) plays a pivotal role in maintaining the homeostasis of essential micronutrients such as manganese (Mn 2+ ), zinc (Zn 2+ ) and iron (Fe 2+ ). Genetic variants of SLC39A8 have been associated with a variety of human diseases, including neuropsychiatric disorders, Crohn's disease, and obesity. To gain insight into ZIP8-mediated metal transport, we generated a homology-based 3D model and identified the amino acid residues constituting metal binding sites 1 (M1) and 2 (M2). Mutagenesis of residues N315, E344 and D318, which form M2, resulted in a complete loss of function, suggesting that M2 plays a central role in the binuclear metal center of ZIP8. Conversely, mutagenesis of residues H314, E343 and D410, which form M1, retained functional activity but with significant alterations: Residue H314 was found to affect substrate selectivity, while residues E344 and D410 were identified as essential for the transport of Fe 2+ and Mn 2+ . Furthermore, residue H347 was found to influence the metal transport turnover rates. These findings indicate that M1 provides accessory functions to ZIP8 activity, including maximal transport rates and/or enhanced substrate selectivity. Furthermore, the present study provides the first direct evidence for Zn 2+ /HCO 3 - cotransport by human ZIP8 and provides insights into HCO 3 - modulation of metal transport. In addition, we have identified a novel metal-binding site, termed M4, formed by residues D311, E348 and D351. Overall, the present study reveals new insights into the structure and metal transport function of ZIP8 and provides a new framework for interpreting functional defects and designing potential therapeutic interventions.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".