A precise spacing between the <scp>NPA</scp> domains of aquaporins is essential for silicon permeability in plants
Bibliographic record
Abstract
The controversy surrounding silicon (Si) benefits and essentiality in plants is exacerbated by the differential ability of species to absorb this element. This property is seemingly enhanced in species carrying specific nodulin 26-like intrinsic proteins (NIPs), a subclass of aquaporins. In this work, our aim was to characterize plant aquaporins to define the features that confer Si permeability. Through comparative analysis of 985 aquaporins in 25 species with differing abilities to absorb Si, we were able to predict 30 Si transporters and discovered that Si absorption is exclusively confined to species that possess NIP-III aquaporins with a GSGR selectivity filter and a precise distance of 108 amino acids (AA) between the asparagine-proline-alanine (NPA) domains. The latter feature is of particular significance since it had never been reported to be essential for Si selectivity. Functionality assessed in the Xenopus oocyte expression system showed that NIPs with 108 AA spacing exhibited Si permeability, while proteins differing in that distance did not. In subsequent functional studies, a Si transporter from poplar mutated into variants with 109- or 107-AA spacing failed to import, and a tomato NIP gene mutated from 109 to 108 AA exhibited a rare gain of function. These results provide a precise molecular basis to classify higher plants into Si accumulators or excluders.
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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".