Interferons: Reprogramming the Metabolic Network against Viral Infection
Bibliographic record
Abstract
Viruses exploit the host and induce drastic metabolic changes to ensure an optimal environment for replication and the production of viral progenies. In response, the host has developed diverse countermeasures to sense and limit these alterations to combat viral infection. One such host mechanism is through interferon signaling. Interferons are cytokines that enhances the transcription of hundreds of interferon-stimulated genes (ISGs) whose products are key players in the innate immune response to viral infection. In addition to their direct targeting of viral components, interferons and ISGs exert profound effects on cellular metabolism. Recent studies have started to illuminate on the specific role of interferon in rewiring cellular metabolism to activate immune cells and limit viral infection. This review reflects on our current understanding of the complex networking that occurs between the virus and host at the interface of cellular metabolism, with a focus on the ISGs in particular, cholesterol-25-hydroxylase (CH25H), spermidine/spermine acetyltransferase 1 (SAT1), indoleamine-2,3-dioxygenase (IDO1) and sterile alpha motif and histidine/aspartic acid domain-containing protein 1 (SAMHD1), which were recently discovered to modulate specific metabolic events and consequently deter viral infection.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".