Efficient mRNA delivery to resting T cells to reverse HIV latency
Bibliographic record
Abstract
Abstract A major hurdle to curing HIV is the persistence of integrated proviruses in resting CD4+ T cells that remain in a transcriptionally silent, latent state. One strategy to eradicate latent HIV is to activate viral transcription, followed by elimination of infected cells through virus-mediated cytotoxicity or immune-mediated clearance. We hypothesised that mRNA-lipid nanoparticle (LNP) technology would provide an opportunity to deliver mRNA encoding proteins able to reverse HIV latency in resting CD4+ T cells. Here we develop an LNP formulation (LNP X) with unprecedented potency to deliver mRNA to hard-to-transfect resting CD4+ T cells in the absence of cellular toxicity or activation. Encapsulating an mRNA encoding the HIV Tat protein, an activator of HIV transcription, LNP X enhances HIV transcription in ex vivo CD4+ T cells from people living with HIV. LNP X further enables the delivery of clustered regularly interspaced short palindromic repeats (CRISPR) activation machinery to modulate both viral and host gene transcription. These findings offer potential for the development of a range of nucleic acid-based T cell therapeutics.
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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 | 0.000 |
| 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 | 0.001 |
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".