Functional characterization of NIPA2, a selective Mg<sup>2+</sup> transporter
Bibliographic record
Abstract
We used microarray analysis to identify renal cell transcripts that were upregulated with low magnesium. One transcript, identified as NIPA2 (nonimprinted in Prader-Willi/Angelman syndrome) subtype 2, was increased over twofold relative to cells cultured in normal magnesium. The deduced sequence comprises 129 amino acids with 8 predicted transmembrane regions. As the secondary structure of NIPA2 conformed to a membrane transport protein, we expressed it in Xenopus oocytes and determined that it mediated Mg(2+) uptake with two-electrode voltage-clamp and fluorescence studies. Mg(2+) transport was electrogenic, voltage dependent, and saturable, demonstrating a Michaelis affinity constant of 0.31 mM. Unlike other reported Mg(2+) transporters, NIPA2 was very selective for the Mg(2+) cation. NIPA2 mRNA is found in many tissues but particularly abundant in renal cells. With the use of immunofluorescence, it was shown that NIPA2 protein was normally localized to the early endosomes and plasma membrane and was recruited to the plasma membrane in response to low extracellular magnesium. We conclude that NIPA2 plays a role in magnesium metabolism and regulation of renal magnesium conservation.
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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 | 0.001 |
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".