Retroviruses As Myeloid Cell Riders: What Natural Human Siglec-1 “Knockouts” Tell Us About Pathogenesis
Bibliographic record
Abstract
Myeloid cells initiate immune responses and are crucial to control infections. In the case of retroviruses, however, myeloid cells also promote pathogenesis by enabling viral dissemination; a process extensively studied in vitro using HIV-1. This viral hijacking mechanism does not rely on productive myeloid cell infection, but requires HIV-1 capture via Siglec-1/CD169, a receptor expressed on myeloid cells that facilitates the infection of bystander target cells. Murine retroviruses are also recognized by Siglec-1, and this interaction is required for robust retroviral infection in vivo. Yet, the relative contribution of Siglec-1-mediated viral dissemination to HIV-1 disease progression remains unclear. The identification of human null individuals lacking working copies of a particular gene enables studying how this loss affects disease progression. Moreover, it can reveal novel antiviral targets whose blockade might be therapeutically effective and safe, since finding null individuals in natura uncovers dispensable functions. We previously described a loss-of-function variant in SIGLEC-1. Analysis of a large cohort of HIV-1-infected individuals identified homozygous and heterozygous subjects, whose cells were functionally null or partially defective for Siglec-1 activity in HIV-1 capture and transmission ex vivo. Nonetheless, analysis of the effect of Siglec-1 truncation on progression to AIDS was not conclusive due to the limited cohort size, the lack of complete clinical records and the restriction to study only off-therapy periods. Here we review how the study of loss-of-function variants might serve to illuminate the role of myeloid cells in viral pathogenesis in vivo and the challenges ahead.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".