Arabidopsis SEIPIN Proteins Modulate Triacylglycerol Accumulation and Influence Lipid Droplet Proliferation
Bibliographic record
Abstract
The lipodystrophy protein SEIPIN is important for lipid droplet (LD) biogenesis in human and yeast cells. In contrast with the single SEIPIN genes in humans and yeast, there are three SEIPIN homologs in Arabidopsis thaliana, designated SEIPIN1, SEIPIN2, and SEIPIN3. Essentially nothing is known about the functions of SEIPIN homologs in plants. Here, a yeast (Saccharomyces cerevisiae) SEIPIN deletion mutant strain and a plant (Nicotiana benthamiana) transient expression system were used to test the ability of Arabidopsis SEIPINs to influence LD morphology. In both species, expression of SEIPIN1 promoted accumulation of large-sized lipid droplets, while expression of SEIPIN2 and especially SEIPIN3 promoted small LDs. Arabidopsis SEIPINs increased triacylglycerol levels and altered composition. In tobacco, endoplasmic reticulum (ER)-localized SEIPINs reorganized the normal, reticulated ER structure into discrete ER domains that colocalized with LDs. N-terminal deletions and swapping experiments of SEIPIN1 and 3 revealed that this region of SEIPIN determines LD size. Ectopic overexpression of SEIPIN1 in Arabidopsis resulted in increased numbers of large LDs in leaves, as well as in seeds, and increased seed oil content by up to 10% over wild-type seeds. By contrast, RNAi suppression of SEIPIN1 resulted in smaller seeds and, as a consequence, a reduction in the amount of oil per seed compared with the wild type. Overall, our results indicate that Arabidopsis SEIPINs are part of a conserved LD biogenesis machinery in eukaryotes and that in plants these proteins may have evolved specialized roles in the storage of neutral lipids by differentially modulating the number and sizes of lipid droplets.
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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 | 0.001 |
| 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".