Aquaporins Mediate Silicon Transport in Humans
Bibliographic record
Abstract
In animals, silicon is an abundant and differentially distributed trace element that is believed to play important biological functions. One would thus expect silicon concentrations in body fluids to be regulated by silicon transporters at the surface of many cell types. Curiously, however, and even though they exist in plants and algae, no such transporters have been identified to date in vertebrates. Here, we show for the first time that the human aquaglyceroporins, i.e., AQP3, AQP7, AQP9 and AQP10 can act as silicon transporters in both Xenopus laevis oocytes and HEK-293 cells. In particular, heterologously expressed AQP7, AQP9 and AQP10 are all able to induce robust, saturable, phloretin-sensitive silicon transport activity in the range that was observed for low silicon rice 1 (lsi1), a silicon transporter in plant. Furthermore, we show that the aquaglyceroporins appear as relevant silicon permeation pathways in both mice and humans based on 1) the kinetics of substrate transport, 2) their presence in tissues where silicon is presumed to play key roles and 3) their transcriptional responses to changes in dietary silicon. Taken together, our data provide new evidence that silicon is a potentially important biological element in animals and that its body distribution is regulated. They should open up original areas of investigations aimed at deciphering the true physiological role of silicon in vertebrates.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".