A-108 The Contribution of memory CD4+ T cell subset phenotype to latency reversal efficiency
Bibliographic record
Abstract
The latent HIV reservoir persists in individuals on ART predominantly in memory CD4+ T cells, a heterogeneous population comprised of central memory (CM), transitional memory (TM) and effector memory (EM) subsets. Current HIV eradication strategies that aim to reverse latency in this heterogeneous pool of cells have had limited success. To characterize HIV latency reversal in all memory CD4+ T cell subsets that contribute to the HIV reservoir in vivo, we developed LARA (Latency and Reversion Assay), a primary cell based in vitro model of HIV latency. To identify pathways associated with latency reversal in each subset, we exposed latently infected cells from both HIV-infected individuals and LARA to different classes of latency reversing agents (LRAs). Memory subsets showed distinct responses that resulted in varying efficiencies to the LRAs tested. Importantly, the most effective LRAs triggered the differentiation into cells that expressed an EM phenotype. Transcriptional profiling of CD4+ T cells from HIV-infected individuals exposed to bryostatin, the LRA that showed the highest latency reversal, identified several EM specific pathways that were significantly upregulated in both the CM and EM subsets, including genes encoding for cytokines and effector molecules such as IFN-γ, IL-2, IL-4, and TNF. Together, these results support LRA exposure triggering differentiation toward an EM subset phenotype to be linked to higher latency reversal efficiency. Identification of these pathways is a critical prerequisite to understand factors that influence latency reversal in vivo as well as contributing to the most effective design of regimens capable of comprehensive reactivation of the HIV reservoir in eradication strategies.
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".