Development of an HIV-1 Specific Microbicide Using Caulobacter crescentus S-Layer Mediated Display of CD4 and MIP1α
Bibliographic record
Abstract
The development of alternative strategies to prevent HIV infection is a global public health priority. Initial efforts in anti-HIV microbicide development have met with poor success as the strategies have relied on a non-specific mechanism of action. Here, we report the development of a microbicide aimed at specifically blocking HIV entry by displaying molecular components of the HIV/host cell attachment complex on the surface of Caulobacter crescentus, a harmless aquatic bacterium. This bacterium can be readily manipulated to present heterologous proteins at high density on its surface by genetic insertion into its crystalline surface layer protein. In separate constructions, we generated bacteria displaying domain 1 of CD4 and MIP1alpha. Each moiety reacted with specific antibodies by Western immunoblot and immuno-fluorescence microscopy. Microbicide functionality was assessed using an HIV pseudotype virus assay system representing Clade B subtypes. Bacteria displaying MIP1alpha reduced infectivity by 35-78% depending on the specific subtype while CD4 display reduced infection by as much as 56%. Combinations of both constructs reduced infectivity by nearly 98%. We demonstrated that HIV infection could be inhibited using a strategy aimed at HIV-specific molecular interactions with Caulobacter surface protein display, and that sufficient protein folding and conformation could be mimicked to bind and block entry. Further, this is the first demonstration that Caulobacter surface protein display may be a useful approach to preventing HIV infection or other viruses as a microbicide. We propose that this harmless bacterium, which is inexpensive to produce and formulate, might be suitable for topical applications as a viable alternative in the search for effective microbicides to counteract the world wide incidence of HIV infection.
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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) | 0.000 | 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".