An HIV-1 Reference Epitranscriptome
Bibliographic record
Abstract
ABSTRACT Post-transcriptional modifications to RNA, which comprise the epitranscriptome, play important roles in RNA metabolism, gene regulation, and human disease, including viral pathogenesis. Modifications to the RNA viral genome and transcripts of human immunodeficiency virus 1 (HIV-1) have been reported and investigated in the context of virus and host biology. However, the diversity of experimental approaches used has made clear correlations across studies, as well as the significance of the HIV-1 epitranscriptome in biology and disease, difficult to assess. Therefore, we established a reference HIV-1 epitranscriptome. We sequenced the model NL4-3 HIV-1 genome from infected primary CD4+ T cells and the Jurkat cell line using the latest nanopore chemistry, optimized RNA preparation methods, and the most current and readily available base-calling algorithms. A highly reproducible sense and a preliminary antisense HIV-1 epitranscriptome were created, where N 6 -methyladenosine (m 6 A), 5-methylcytosine (m 5 C), pseudouridine (psi), inosine, and 2’- O -methyl (N m ) modifications could be identified by rapid multiplexed base-calling. We observed that sequence and neighboring modification contexts induced modification miscalling, which could be corrected with synthetic HIV-1 RNA fragments. We validated m 6 A modification sites with STM2457, a small molecule inhibitor of methyltransferase-like 3 (METTL3). We find that modifications are quite stable under combination antiretroviral therapy (cART) treatment, in primary CD4+ T cells, and in HIV-1 virions. Sequencing samples from people living with HIV (PLWH) revealed conservation of m 6 A modifications. However, analysis of spliced transcript variants suggests transcript-dependent modification levels. Our approach and reference data offer a straightforward benchmark that can be adopted to help advance rigor, reproducibility, and uniformity across HIV-1 epitranscriptomics studies. They also provide a roadmap for the creation of reference epitranscriptomes for many other viruses or pathogens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".