Lipid Droplet-Associated Proteins (LDAPs) Are Required for the Dynamic Regulation of Neutral Lipid Compartmentation in Plant Cells
Bibliographic record
Abstract
Eukaryotic cells compartmentalize neutral lipids into organelles called lipid droplets (LDs), and while much is known about the role of LDs in storing triacylglycerols in seeds, their biogenesis and function in nonseed tissues are poorly understood. Recently, we identified a class of plant-specific, lipid droplet-associated proteins (LDAPs) that are abundant components of LDs in nonseed cell types. Here, we characterized the three LDAPs in Arabidopsis (Arabidopsis thaliana) to gain insight to their targeting, assembly, and influence on LD function and dynamics. While all three LDAPs targeted specifically to the LD surface, truncation analysis of LDAP3 revealed that essentially the entire protein was required for LD localization. The association of LDAP3 with LDs was detergent sensitive, but the protein bound with similar affinity to synthetic liposomes of various phospholipid compositions, suggesting that other factors contributed to targeting specificity. Investigation of LD dynamics in leaves revealed that LD abundance was modulated during the diurnal cycle, and characterization of LDAP misexpression mutants indicated that all three LDAPs were important for this process. LD abundance was increased significantly during abiotic stress, and characterization of mutant lines revealed that LDAP1 and LDAP3 were required for the proper induction of LDs during heat and cold temperature stress, respectively. Furthermore, LDAP1 was required for proper neutral lipid compartmentalization and triacylglycerol degradation during postgerminative growth. Taken together, these studies reveal that LDAPs are required for the maintenance and regulation of LDs in plant cells and perform nonredundant functions in various physiological contexts, including stress response and postgerminative growth.
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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) | 0.001 | 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 | 0.001 |
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".