PEMT Mediates Hepatitis C Virus-Induced Steatosis, Explains Genotype-Specific Phenotypes and Supports Virus Replication
Bibliographic record
Abstract
The hepatitis C virus (HCV) relies on cellular lipid pathways for virus replication and also induces liver steatosis, but the mechanisms involved are not clear. We performed a quantitative lipidomics analysis of virus-infected cells by combining high-performance thin-layer chromatography (HPTLC) and mass spectrometry, using an established HCV cell culture model and subcellular fractionation. Neutral lipid and phospholipids were increased in the HCV-infected cells; in the endoplasmic reticulum there was an ~four-fold increase in free cholesterol and an ~three-fold increase in phosphatidyl choline (p < 0.05). The increase in phosphatidyl choline was due to the induction of a non-canonical synthesis pathway involving phosphatidyl ethanolamine transferase (PEMT). An HCV infection induced expression of PEMT while knocking down PEMT with siRNA inhibited virus replication. As well as supporting virus replication, PEMT mediates steatosis. Consistently, HCV induced the expression of the pro-lipogenic genes SREBP 1c and DGAT1 while inhibiting the expression of MTP, promoting lipid accumulation. Knocking down PEMT reversed these changes and reduced the lipid content in virus-infected cells. Interestingly, PEMT expression was over 50% higher in liver biopsies from people infected with the HCV genotype 3 than 1, and three times higher than in people with chronic hepatitis B, suggesting that this may account for genotype-dependent differences in the prevalence of hepatic steatosis. PEMT is a key enzyme for promoting the accumulation of lipids in HCV-infected cells and supports virus replication. The induction of PEMT may account for virus genotype specific differences in hepatic steatosis.
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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 | 0.001 |
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".