Compensation by the E138K Mutation in HIV-1 Reverse Transcriptase for Deficits in Viral Replication Capacity and Enzyme Processivity Associated with the M184I/V Mutations
Bibliographic record
Abstract
Recently, several phase 3 clinical trials (ECHO and THRIVE) showed that E138K and M184I were the most frequent mutations to emerge in patients who failed therapy with rilpivirine (RPV) together with two nucleos(t)ide reverse transcriptase inhibitors, emtricitabine (FTC) and tenofovir (TDF). To investigate the basis for the copresence of E138K and M184I, we generated recombinant mutated and wild-type (WT) reverse transcriptase (RT) enzymes and HIV-1(NL4-3) infectious clones. Drug susceptibilities were determined in cord blood mononuclear cells (CBMCs). Structural modeling was performed to analyze any impact on deoxynucleoside triphosphate (dNTP) binding. The results of phenotyping showed that viruses containing both the E138K and M184V mutations were more resistant to each of FTC, 3TC, and ETR than viruses containing E138K and M184I. Viruses with E138K displayed only modest resistance to ETR, little resistance to efavirenz (EFV), and no resistance to either FTC or 3TC. E138K restored viral replication capacity (RC) in the presence of M184I/V, and this was confirmed in cell-free RT processivity assays. RT enzymes containing E138K, E138K/184I, or E138K/184V exhibited higher processivity than WT RT at low dNTP concentrations. Steady-state kinetic analysis demonstrated that the E138K mutation resulted in decreased K(m)s for dNTPs. In contrast, M184I/V resulted in an increased K(m) for dNTPs compared to those for WT RT. These results indicate that the E138K mutation compensates for both the deficit in dNTP usage and impairment in replication capacity by M184I/V. Structural modeling shows that the addition of E138K to M184I/V promotes tighter dNTP binding.
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 | 0.001 |
| 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".