Anti-HIV-1 Activity of Elafin Is More Potent than Its Precursor's, Trappin-2, in Genital Epithelial Cells
Bibliographic record
Abstract
Cervicovaginal lavage fluid (CVL) is a natural source of anti-HIV-1 factors; however, molecular characterization of the anti-HIV-1 activity of CVL remains elusive. In this study, we confirmed that CVLs from HIV-1-resistant (HIV-R) compared to HIV-1-susceptible (HIV-S) commercial sex workers (CSWs) contain significantly larger amounts of serine antiprotease trappin-2 (Tr) and its processed form, elafin (E). We assessed anti-HIV-1 activity of CVLs of CSWs and recombinant E and Tr on genital epithelial cells (ECs) that possess (TZM-bl) or lack (HEC-1A) canonical HIV-1 receptors. Our results showed that immunodepletion of 30% of Tr/E from CVL accounted for up to 60% of total anti-HIV-1 activity of CVL. Knockdown of endogenous Tr/E in HEC-1A cells resulted in significantly increased shedding of infectious R5 and X4 HIV-1. Pretreatment of R5, but not X4 HIV-1, with either Tr or E led to inhibition of HIV-1 infection of TZM-bl cells. Interestingly, when either HIV-1 or cells lacking canonical HIV-1 receptors were pretreated with Tr or E, HIV-1 attachment and transcytosis were significantly reduced, and decreased attachment was not associated with altered expression of syndecan-1 or CXCR4. Determination of 50% inhibitory concentrations (IC(50)) of Tr and E anti-HIV-1 activity indicated that E is ∼130 times more potent than its precursor, Tr, despite their equipotent antiprotease activities. This study provides the first experimental evidence that (i) Tr and E are among the principal anti-HIV-1 molecules of CVL; (ii) Tr and E affect cell attachment and transcytosis of HIV-1; (iii) E is more efficient than Tr regarding anti-HIV-1 activity; and (iv) the anti-HIV-1 effect of Tr and E is contextual.
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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 | 0.000 |
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".