Microarray analysis of gene expression in the liver of transgenic mouse model of HCV infection
Bibliographic record
Abstract
Background: The molecular interactions of hepatitis C virus (HCV) with hepatic tissue have yet to be completely elucidated and understood. The purpose of this study was to compare differential gene expression patterns in the livers of non-transgenic and transgenic mouse model expressing HCV structural proteins Core, Envelope 1 (E1) and Envelope 2 (E2) using complementary DNA (cDNA) microarrays. Results: Total RNA extracted from the livers of HCV transgenic and non-transgenic mice was analyzed with cDNA microarray and differentially expressed genes confirmed by real-time RT-PCR. Relative expression ratios of individual genes were determined by comparing hybridization of Cy5-labelled cDNA from transgenic mouse livers and Cy3-labelled cDNA from non-transgenic mouse livers. The spot array images were quantified using QuantArray software and the outlier spots was normalized and filtered using five different criteria. 15,297 genes were analyzed using three different analytical methods. Depending on these methods, twenty-one genes were found to be differentially expressed at a statistically significant level. From these, 6 genes had a consistent differential expression. Several genes were directly involved in lipid metabolism and lipid β-oxidation. 5-azacytidine induced gene 2 (AZ2), which is involved in the methylation of genes was down regulated in HCV transgenic mice. Altered transcript levels of these 6 genes were confirmed by real-time RT-PCR analysis. Conclusion: Interactions between HCV and hepatocytes not only involve lipid metabolism and redox balance, but this interaction may also influence DNA methylation, indicating a potential association with the development of hepatocellular carcinoma.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".