Alterations in phosphatidylethanolamine metabolism impacts hepatocellular lipid storage, energy homeostasis, and proliferation
Bibliographic record
Abstract
Phosphatidylethanolamine (PE) is the second most abundant glycerophospholipid in eukaryotic membranes and is involved in several cellular processes. An important pathway for de novo PE synthesis is the Kennedy Pathway. The rate limiting enzyme in the pathway, CTP:phosphoethanolamine cytidyltransferase, catalyzes the synthesis of CDP-ethanolamine from phosphoethanolamine (pEtn) and CTP. Ethanolamine phosphate phospholyase (ETNPPL) has the potential to breakdown pEtn by catabolizing it to form acetaldehyde, ammonium, and inorganic phosphate. Research on this enzyme is limited and it is unclear how its activity affects PE synthesis; therefore, an investigation into ETNPPL's biological effects is required. ETNPPL was expressed in human hepatoma cell line (Huh7) by stable transfection, allowing for long-term expression in cells without ETNPPL. We show that ETNPPL reduces cellular pEtn synthesized from ethanolamine, which decreased synthesis of PE and an increased PC:PE ratio, which has been shown to be associated with metabolic dysfunction-associated steatotic liver disease (MASLD) and impaired mitochondrial function. Experiments conducted show increased neutral lipid storage accompanied by decreased ATP production and oxygen consumption; however, no differences in triglyceride secretion were seen, although ApoB100 secretion was reduced. Huh7 cells expressing ETNPPL proliferate at a slower rate than control and have increased mRNA expression of p53 and tumor suppressor genes (CDKN1A, BBC3, BAX, BRCA1), implicating ETNPPL in cell proliferation, cancer development and/or tumor progression. Overall, ETNPPL rewires hepatic lipid metabolism, altering several processes including increasing lipid storage and decreasing proliferation. The impacts observed in this study may create a link between hepatic ETNPPL expression and MASLD/HCC pathophysiology.
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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) | 0.001 | 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".