Molecular identification of ancient and modern mammalian magnesium transporters
Bibliographic record
Abstract
A large number of mammalian Mg(2+) transporters have been hypothesized on the basis of physiological data, but few have been investigated at the molecular level. The recent identification of a number of novel proteins that mediate Mg(2+) transport has enhanced our understanding of how Mg(2+) is translocated across mammalian membranes. Some of these transporters have some similarity to those found in prokaryocytes and yeast cells. Human Mrs2, a mitochondrial Mg(2+) channel, shares many of the properties of the bacterial CorA and yeast Alr1 proteins. The SLC41 family of mammalian Mg(2+) transporters has a similarity with some regions of the bacterial MgtE transporters. The mammalian ancient conserved domain protein (ACDP) Mg(2+) transporters are found in prokaryotes, suggesting an ancient origin. However, other newly identified mammalian transporters, including TRPM6/7, MagT, NIPA, MMgT, and HIP14 families, are not represented in prokaryotic genomes, suggesting more recent development. MagT, NIPA, MMgT, and HIP14 transporters were identified by differential gene expression using microarray analysis. These proteins, which are found in many different tissues and subcellular organelles, demonstrate a diversity of structural properties and biophysical functions. The mammalian Mg(2+) transporters have no obvious amino acid similarities, indicating that there are many ways to transport Mg(2+) across membranes. Most of these proteins transport a number of divalent cations across membranes. Only MagT1 and NIPA2 are selective for Mg(2+). Many of the identified mammalian Mg(2+) transporters are associated with a number of congenital disorders encompassing a wide range of tissues, including intestine, kidney, brain, nervous system, and skin. It is anticipated that future research will identify other novel Mg(2+) transporters and reveal other diseases.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".