Bibliographic record
Abstract
Although cytoplasmic lipid droplets (LDs) are the major reserves for energy-dense neutral lipids in plants, the cellular mechanisms for packaging neutral lipids into LDs remain poorly understood. To gain insights into the cellular processes of neutral lipid accumulation and compartmentalization, a necessary step forward would be to characterize functional roles of lipogenic proteins that participate in the compartmentalization of neutral lipids in plant cells. In this study, the lipogenic proteins, Arabidopsis thaliana SEIPIN homologues and mouse (Mus Musculus) fat storage-inducing transmembrane protein 2 (FIT2), were characterized for their functional roles in the biogenesis of cytoplasmic LDs in various plant tissues. Both Arabidopsis SEIPINs and mouse FIT2 supported the accumulation of neutral lipids and cytoplasmic LDs in plants. The three Arabidopsis SEIPIN isoforms play distinct roles in compartmentalizing neutral lipids by enhancing the numbers and sizes of LDs in various plant tissues and developmental stages. Further, the potential applications of Arabidopsis SEIPINs and mouse FIT2 in engineering neutral lipids and terpenes in plant vegetative tissues were evaluated by co-expressing these and other lipogenic proteins in Nicotiana benthamiana leaves. Arabidopsis SEIPINs and mouse FIT2 represent effective tools that may complement ongoing strategies to enhance the accumulation of desired neutral lipids and terpenes in plant vegetative tissues. Collectively, our findings in this study expand our knowledge of the broader cellular mechanisms of LD biogenesis that are partially conserved in eukaryotes and distinct in plants and suggest novel targets that can be introduced into plants to collaborate with other factors in lipid metabolism and elevate oil content in plant tissues.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
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".