Impact of the griffithsin anti-HIV microbicide and placebo gels on the rectal mucosal proteome and microbiome in non-human primates
Bibliographic record
Abstract
Topical microbicides are being explored as an HIV prevention method for individuals who practice receptive anal intercourse. In vivo studies of these microbicides are critical to confirm safety. Here, we evaluated the impact of a rectal microbicide containing the antiviral lectin, Griffithsin (GRFT), on the rectal mucosal proteome and microbiome. Using a randomized, crossover placebo-controlled design, six rhesus macaques received applications of hydroxyethylcellulose (HEC)- or carbopol-formulated 0.1% GRFT gels. Rectal mucosal samples were then evaluated by label-free tandem MS/MS and 16 S rRNA gene amplicon sequencing, for proteomics and microbiome analyses, respectively. Compared to placebo, GRFT gels were not associated with any significant changes to protein levels at any time point (FDR < 5%), but increased abundances of two common and beneficial microbial taxa after 24 hours were observed in HEC-GRFT gel (p < 2E-09). Compared to baseline, both placebo formulations were associated with alterations to proteins involved in proteolysis, activation of the immune response and inflammation after 2 hours (p < 0.0001), and increases in beneficial Faecalibacterium spp. after 24 hours in HEC placebo gel (p = 4.21E-15). This study supports the safety profile of 0.1% GRFT gel as an anti-HIV microbicide and demonstrates that current placebo formulations may associate with changes to rectal proteome and microbiota.
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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) | 0.002 | 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".