Activation of sterol regulatory element-binding protein 1c and fatty acid synthase transcription by hepatitis C virus non-structural protein 2
Bibliographic record
Abstract
Transcriptional factor sterol regulatory element-binding protein 1c (SREBP-1c) activates the transcription of lipogenic genes, including fatty acid synthase (FAS). Hepatitis C virus (HCV) infection is often associated with lipid accumulation within the liver, known as steatosis in the clinic. The molecular mechanisms of HCV-associated steatosis are not well characterized. Here, we showed that HCV non-structural protein 2 (NS2) activated SREBP-1c transcription in human hepatic Huh-7 cells as measured by using a human SREBP-1c promoter-luciferase reporter plasmid. We further showed that sterol regulatory element (SRE) and liver X receptor element (LXRE) in the SREBP-1c promoter were involved in SREBP-1c activation by HCV NS2. Furthermore, expression of HCV NS2 resulted in the upregulation of FAS transcription. We also showed that FAS upregulation by HCV NS2 was SREBP-1-dependent since deleting the SRE sequence in a FAS promoter and expressing a dominant-negative SREBP-1 abrogated FAS promoter upregulation by HCV NS2. Taken together, our results suggest that HCV NS2 can upregulate the transcription of SREBP-1c and FAS, and thus is probably a contributing factor for HCV-associated steatosis.
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.
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) | 0.003 | 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".