Medium-Chain Polyprenols Influence Chloroplast Membrane Dynamics in Solanum lycopersicum
Bibliographic record
Abstract
The widespread occurrence of polyprenols throughout the plant kingdom is well documented, yet their functional role is poorly understood. These lipophilic compounds are known to be assembled from isoprenoid precursors by a class of enzymes designated as cis-prenyltransferases (CPTs), which are encoded by small CPT gene families in plants. In this study, we report that RNA interference (RNAi)-mediated knockdown of one member of the tomato CPT family (SlCPT5) reduced polyprenols in leaves by about 70%. Assays with recombinant SlCPT5 produced in Escherichia coli determined that the enzyme synthesizes polyprenols of approximately 50-55 carbons (Pren-10, Pren-11) in length and accommodates a variety of trans-prenyldiphosphate precursors as substrates. Introduction of SlCPT5 into the polyprenol-deficient yeast Δrer2 mutant resulted in the accumulation of Pren-11 in yeast cells, restored proper protein N-glycosylation and rescued the temperature-sensitive growth phenotype that is associated with its polyprenol deficiency. Subcellular fractionation studies together with in vivo localization of SlCPT5 fluorescent protein fusions demonstrated that SlCPT5 resides in the chloroplast stroma and that its enzymatic products accumulate in both thylakoid and envelope membranes. Transmission electron microscopy images of polyprenol-deficient leaves revealed alterations in chloroplast ultrastructure, and anisotropy measurements revealed a more disordered state of their envelope membranes. In polyprenol-deficient leaves, CO2 assimilation was hindered and their thylakoid membranes exhibited lower phase transition temperatures and calorimetric enthalpies, which coincided with a decreased photosynthetic electron transport rate. Taken together, these results uncover a role for polyprenols in governing chloroplast membrane dynamics.
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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".