Metabolic Control by DNA Tumor Virus-Encoded Proteins
Bibliographic record
Abstract
Viruses co-opt a multitude of host cell metabolic processes in order to meet the energy and substrate requirements for successful viral replication. However, due to their limited coding capacity, viruses must enact most, if not all, of these metabolic changes by influencing the function of available host cell regulatory proteins. Typically, certain viral proteins, some of which can function as viral oncoproteins, interact with these cellular regulatory proteins directly in order to effect changes in downstream metabolic pathways. This review highlights recent research into how four different DNA tumor viruses, namely human adenovirus, human papillomavirus, Epstein-Barr virus and Kaposi's associated-sarcoma herpesvirus, can influence host cell metabolism through their interactions with either MYC, p53 or the pRb/E2F complex. Interestingly, some of these host cell regulators can be activated or inhibited by the same virus, depending on which viral oncoprotein is interacting with the regulatory protein. This review highlights how MYC, p53 and pRb/E2F regulate host cell metabolism, followed by an outline of how each of these DNA tumor viruses control their activities. Understanding how DNA tumor viruses regulate metabolism through viral oncoproteins could assist in the discovery or repurposing of metabolic inhibitors for antiviral therapy or treatment of virus-dependent cancers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".