Si permeability of a deficient Lsi1 aquaporin in tobacco can be enhanced through a conserved residue substitution
Bibliographic record
Abstract
Abstract Silicon (Si) is a beneficial substrate for many plants, conferring heightened resilience to environmental stress. A plant's ability to absorb Si is primarily dependent on the presence of a Si‐permeable Lsi1 (NIP2‐1) aquaporin in its roots. Structure‐function analyses of Lsi1 channels from higher plants have thus far revealed two key molecular determinants of Si permeability: (a) the amino acid motif GSGR in the aromatic/arginine selectivity filter and (b) 108 amino acids between two highly conserved NPA domains. Curiously, tobacco ( Nicotiana sylvestris ) stands as a rare exception as it possesses an Lsi1 (NsLsi1) with these molecular signatures but is reported as a low Si accumulator. The aim of this study was therefore to identify whether additional determinants influence Si permeability via Lsi1 channels, focusing on the role of residues that differ uniquely in NsLsi1 relative to functional Lsi1 homologs. We observed tobacco indeed absorbed Si poorly (0.1% dw), despite NsLsi1 being expressed constitutively in planta . Si influx measured in NsLsi1‐expressing Xenopus oocytes was very low (<13% that of OsLsi1 from rice ( Oryza sativa ) over a 3‐hr time course), which likely explains why tobacco is a low Si accumulator. Interestingly, NsLsi1 P125F displayed a significant gain of function (threefold increase in Si influx relative to NsLsi1 WT ), which coincided with a threefold increase in plasma membrane localization in planta , as measured by transient expression of GFP constructs in Nicotiana benthamiana leaves. These findings thus reveal a novel molecular determinant of Si transport in plants and inform breeding, biotechnological, and agricultural practices to effectively utilize Si in the context of plant resilience to environmental stress.
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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".