Predicting Treatment Response and the Role of the ISG15/USP18 Ubiquitin-like Signaling Pathway in Hepatitis C Viral Infection
Bibliographic record
Abstract
Hepatitis C Virus (HCV) infects 170 million people worldwide. The current treatment regimen, which is combination therapy with pegylated interferon (PegIFN) and Ribavirin (Rib), cures only 50% of the patients infected with the most prevalent HCV genotype. Therefore, there is a pressing need to understand the molecular mechanism of interferon resistance and to develop a prognostic tool to predict who will respond to treatment before initiation of therapy. It has been firmly established that the virus-host interaction plays an important role in determining treatment outcomes. My thesis investigated the host factors that are involved in interferon resistance with an aim to provide insights into the molecular mechanism of IFN resistance. \ncDNA microarray analysis identified 18 differentially expressed hepatic genes from pretreatment liver tissues of responders (Rs) and non-responders (NRs). Based on the differential expression levels of these 18 genes, a prognostic tool was developed to predict who will respond to therapy, with a positive predicting value (PPV) of 96%. Most of these 18 genes are interferon stimulated genes (ISGs) and they are more highly expressed in NR livers, indicating that preactivation of interferon signaling in the pre-treatment liver tissues contributes to NR. 3 out of the 18 genes are involved in an ubiquitin-like ISG15/USP18 signaling pathway that plays an important role in interferon response. Over-expression of USP18 and ISG15 in the pretreatment liver tissues of NR promotes HCV production and blunts interferon anti-HCV activity. There exists a distinct cell-type specific ISG activation in the pretreatment liver tissues of Rs and NRs. Up-regulation of the two ISGs that I tested (ISG15 and MxA) was found mainly in hepatocytes in NRs while ISG activation was preferentially observed in macrophages in Rs.\nTaking all these data together, pre-activation of interferon signaling and cell-type specific gene activation in the pretreatment liver tissues of patients infected with HCV are associated with treatment non-response. HCV exploits the host interferon system to favour its persistence by enhanced replication /secretion stimulated by a few ISGs (ISG15, USP18) in response to IFN. The developed prognostic tool can be used to stratify patients for treatment and the novel insights of the molecular mechanism of IFN resistance in HCV patients offer potential drug targets for future development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".