Discovery of a multigene family of aquaporin silicon transporters in the primitive plant <i>Equisetum arvense</i>
Bibliographic record
Abstract
Plants benefit greatly from silicon (Si) absorption provided that they contain Si transporters. The latter have recently been identified in the roots of some higher plants known to accumulate high concentrations of Si, and all share a high level of sequence identity. In this study, we searched for transporters in the primitive vascular plant Equisetum arvense (horsetail), which is a valuable but neglected model plant for the study of Si absorption, as it has one of the highest Si concentrations in the plant kingdom. Our initial attempts to identify Si transporters based on sequence homology with transporters from higher plants proved unsuccessful, suggesting a divergent structure or property in horsetail transporters. Subsequently, through sequencing of the horsetail root transcriptome and a search using amino acid sequences conserved in plant aquaporins, we were able to identify a multigene family of aquaporin Si transporters. Comparison of known functional domains and phylogenetic analysis of sequences revealed that the horsetail proteins belong to a different group than higher-plant Si transporters. In particular, the newly identified proteins contain a STAR pore as opposed to the GSGR pore common to all previously identified Si transporters. In order to determine its functionality, the proteins were heterologously expressed in both Xenopus oocytes and Arabidopsis, and the results showed that the horsetail proteins are extremely efficient a transporting Si. These findings offer new insights into the elusive properties of Si and its absorption by plants.
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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".