Nuclear lipid droplets in Caco2 cells originate from nascent precursors and in situ at the nuclear envelope
Bibliographic record
Abstract
Intestinal epithelial cells convert excess fatty acids into triglyceride (TAG) for storage in cytoplasmic lipid droplets and secretion in chylomicrons. Nuclear lipid droplets (nLDs) are present in intestinal cells but their origin and relationship to cytoplasmic TAG synthesis and secretion is unknown. nLDs and related lipid-associated promyelocytic leukemia structures (LAPS) were abundant in oleate-treated Caco2 but less frequent in other human colorectal cancer cell lines and mouse intestinal organoids. nLDs and LAPS in undifferentiated oleate-treated Caco2 cells harbored the phosphatidate phosphatase Lipin1, its product diacylglycerol, and CTP:phosphocholine cytidylyltransferase (CCT)α. CCTα knockout Caco2 cells had fewer but larger nLDs, indicating a reliance on de novo PC synthesis for assembly. Differentiation of Caco2 cells caused large nLDs and LAPS to form regardless of oleate treatment or CCTα expression. nLDs and LAPS in Caco2 cells did not associate with apoCIII and apoAI and formed dependently of microsomal triglyceride transfer protein expression and activity, indicating they are not derived from endoplasmic reticulum luminal LDs precursors. Instead, undifferentiated Caco2 cells harbored a constitutive pool of nLDs and LAPS in proximity to the nuclear envelope that expanded in size and number with oleate treatment. Inhibition of TAG synthesis did affect the number of nascent nLDs and LAPS but prevented their association with promyelocytic leukemia protein, Lipin1α, and diacylglycerol, which instead accumulated on the nuclear membranes. Thus, nLD and LAPS biogenesis in Caco2 cells is not linked to lipoprotein secretion but involves biogenesis and/or expansion of nascent nLDs by de novo lipid synthesis.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".