Lipid Droplet–Peroxisome Connections in Plants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it