Lipid Droplet–Peroxisome Connections in Plants
Bibliographic record
Abstract
Lipid droplets (LDs) are the principal subcellular sites for the storage of triacylglycerols (TAGs), and in plants, TAG degradation requires metabolism in peroxisomes. This metabolic cooperation includes TAG hydrolysis by the sugar-dependent 1 lipase located on the LD surface and the transfer of fatty acids into the peroxisome matrix by the peroxisomal membrane ATP-binding cassette transporter, PXA1. During seed germination, this process fuels heterotrophic growth and involves the retromer-dependent formation of peroxisomal membrane extensions called peroxules that interact with LDs. Similar changes in membrane architecture are also observed during interactions of peroxisomes and LDs in yeast and mammalian cells, despite differences in the molecular components required for their connections. Proteins directly involved in LD–peroxisome membrane contact site formation in plants have not yet been identified, but the connection between these two organelles is dependent upon PXA1, which contains a cytoplasmic exposed FFAT (two phenylalanines in an acidic tract)-like motif capable of interacting with vesicle-associated membrane protein-associated proteins (VAPs). Indeed, the identification of several VAPs in plant LD proteomes supports the premise that a VAP-PXA1 connection might be part of a functional tethering complex that connects these two organelles, although other types of interactions are also possible. Overall, such connections between peroxisomes and LDs would allow for efficient transfer of lipophilic substrates from LDs to the peroxisome matrix in plant cells, similar to how VAPs participate in lipid transfer reactions between other subcellular compartments in eukaryotic systems.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".