Endometrial Epithelial Cell Responses to Coinfecting Viral and Bacterial Pathogens in the Genital Tract Can Activate the HIV-1 LTR in an NFκB-and AP-1–Dependent Manner
Bibliographic record
Abstract
BACKGROUND: Sexually transmitted infections (STIs) are associated with increased human immunodeficiency virus type 1 (HIV-1) susceptibility and viral shedding in the genital tract, but the mechanisms underlying this association are poorly understood. METHODS: Direct activation of HIV long terminal repeats (LTRs), a proxy measure for HIV-1 replication, was measured after treatment of 1G5 T cells with Toll-like receptor (TLR) ligands, herpes simplex virus type 1 or 2 (HSV-1/2), or Neisseria gonorrhoeae. For indirect activation, 1G5 T cells were incubated with supernatants from female primary genital epithelial cells (GECs) previously exposed to these agents. Proinflammatory cytokines and chemokines were measured in GEC supernatants. Proinflammatory pathways were blocked to determine the mechanisms of direct and indirect HIV-LTR activation. RESULTS: HSV-1/2, N. gonorrhoeae, and TLR ligands FimH (TLR-4), flagellin (TLR-5), and Poly (I:C) (TLR-3) directly induced HIV-LTR activation in 1G5 T cells. Supernatants collected from GECs incubated with these agents indirectly induced HIV-LTR activation. Production of tumor necrosis factor α, interleukin 6, interleukin 8, and monocyte chemoattractant protein-1 was elevated in GECs exposed to copathogens. Inhibition of nuclear factor κB and activator protein-1 (AP-1) signaling pathways in 1G5 T cells abrogated both direct and indirect HIV-LTR activation. CONCLUSIONS: STIs may increase HIV-1 replication in the female genital tract via proinflammatory signaling pathways directly and indirectly via their effects on GECs. This increased HIV-1 replication may enhance sexual and vertical HIV transmission.
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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) | 0.003 | 0.000 |
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".