In vivo gene delivery and expression by bacteriophage lambda vectors
Bibliographic record
Abstract
AIMS: Bacteriophage vectors have potential as gene transfer and vaccine delivery vectors because of their low cost, safety and physical stability. However, little is known concerning phage-mediated gene transfer in mammalian hosts. We therefore performed experiments to examine phage-mediated gene transfer in vivo. METHODS AND RESULTS: Mice were inoculated with recombinant lambda phage containing a mammalian expression cassette encoding firefly luciferase (luc). Efficient, dose-dependent in vivo luc expression was detected, which peaked within 24 h of delivery and declined to undetectable levels within a week. Display of an integrin-binding peptide increased cellular internalization of phage in vitro and enhanced phage-mediated gene transfer in vivo. Finally, in vivo depletion of phagocytic cells using clodronate liposomes had only a minor effect on the efficiency of phage-mediated gene transfer. CONCLUSIONS: Unmodified lambda phage particles are capable of transducing mammalian cells in vivo, and may be taken up -- at least in part -- by nonphagocytic mechanisms. Surface modifications that enhance phage uptake result in more efficient in vivo gene transfer. SIGNIFICANCE AND IMPACT OF THE STUDY: These experiments shed light on the mechanisms involved in phage-mediated gene transfer in vivo, and suggest new approaches that may enhance the efficiency of this process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".