Identification of Low-Abundance Lipid Droplet Proteins in Seeds and Seedlings
Bibliographic record
Abstract
The developmental program of seed formation, germination, and early seedling growth requires not only tight regulation of cell division and metabolism, but also concerted control of the structure and function of organelles, which relies on specific changes in their protein composition. Of particular interest is the switch from heterotrophic to photoautotrophic seedling growth, for which cytoplasmic lipid droplets (LDs) play a critical role as depots for energy-rich storage lipids. Here, we present the results of a bottom-up proteomics study analyzing the total protein fractions and LD-enriched fractions in eight different developmental phases during silique (seed) development, seed germination, and seedling establishment in Arabidopsis (Arabidopsis thaliana). The quantitative analysis of the LD proteome using LD-enrichment factors led to the identification of six previously unidentified and comparably low-abundance LD proteins, each of which was confirmed by intracellular localization studies with fluorescent protein fusions. In addition to these advances in LD protein discovery and the potential insights provided to as yet unexplored aspects in plant LD functions, our data set allowed for a comparative analysis of the LD protein composition throughout the various developmental phases examined. Among the most notable of the alterations in the LD proteome were those during seedling establishment, indicating a switch in the physiological function(s) of LDs after greening of the cotyledons. This work highlights LDs as dynamic organelles with functions beyond lipid storage.
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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 | 0.000 |
| 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 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".