Unique intrahepatic transcriptomics profiles discriminate the clinical phases of a chronic HBV infection
Bibliographic record
Abstract
Chronic hepatitis B is a highly heterogeneous liver disease characterized by phases with fluctuations in viral replication and progressive liver damage in some, but not all infected individuals. Despite four decades of research, insight into host determinants underlying these distinct clinical phases-immunotolerant, immune active, inactive carrier, and HBeAg-negative hepatitis-remains elusive. We performed an in-depth transcriptome analysis of archived FFPE liver biopsies of each clinical phase to address host determinants associated with the natural history. Therefore, we determined, for the first time, intrahepatic global expression profiles of well-characterized chronic HBV patients at different clinical phases. Our data, obtained by microarray, demonstrate that B cells and NK/cytotoxic-related genes in the liver, including CD19, TNFRSF13C, GZMH, and KIR2DS3, were differentially expressed across the clinical HBV phases, which was confirmed by modular analysis and also Nanostring arrays in an independent cohort. Compared to the immunotolerant phase, 92 genes were differentially expressed in the liver during the immune active phase, 46 in the inactive carrier phase, and 71 in the HBeAg-negative phase. Furthermore, our study also revealed distinctive transcription of genes associated with cell cycle activity, NF-κB signaling, cytotoxic function and mitochondrial respiration between clinical phases. Our data define for the first time using microarray unique transcriptomes in the HBV-infected liver during consecutive clinical phases. We demonstrate that fluctuations of viral loads and liver damage coincide with fluctuations in the liver transcriptome and point to functional- immune and non-immune- components contributing to the clinical phenotype in patients.
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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".