Biochemical, inhibition and inhibitor resistance studies of xenotropic murine leukemia virus-related virus reverse transcriptase
Bibliographic record
Abstract
We report key mechanistic differences between the reverse transcriptases (RT) of human immunodeficiency virus type-1 (HIV-1) and of xenotropic murine leukemia virus-related virus (XMRV), a gammaretrovirus that can infect human cells. Steady and pre-steady state kinetics demonstrated that XMRV RT is significantly less efficient in DNA synthesis and in unblocking chain-terminated primers. Surface plasmon resonance experiments showed that the gammaretroviral enzyme has a remarkably higher dissociation rate (k(off)) from DNA, which also results in lower processivity than HIV-1 RT. Transient kinetics of mismatch incorporation revealed that XMRV RT has higher fidelity than HIV-1 RT. We identified RNA aptamers that potently inhibit XMRV, but not HIV-1 RT. XMRV RT is highly susceptible to some nucleoside RT inhibitors, including Translocation Deficient RT inhibitors, but not to non-nucleoside RT inhibitors. We demonstrated that XMRV RT mutants K103R and Q190M, which are equivalent to HIV-1 mutants that are resistant to tenofovir (K65R) and AZT (Q151M), are also resistant to the respective drugs, suggesting that XMRV can acquire resistance to these compounds through the decreased incorporation mechanism reported in HIV-1.
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) | 0.000 | 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".