Improvement of Gene Delivery by Minimal Bacteriophage Particles
Bibliographic record
Abstract
Direct delivery of therapeutic genes is a promising approach for treating cancers and other diseases. The current human viral vectors, however, suffer from several drawbacks, including poor cell-type specificity and difficult large-scale production. The M13 phage provides an alternative vehicle for gene therapy with engineerable specificity, but the low transduction efficiency seriously limits its translational application. In this work, we discovered important factors of cells and phages that greatly influence the phage transduction. The up-regulation of PrimPol or the down-regulation of DMBT1 in cells significantly enhanced the phage transduction efficiency. Furthermore, we found that the phage transduction efficiency was inversely correlated with the phage size. By carefully reconstructing the phage origin with the gene of interest, we designed “TransPhage” with a minimal length and maximal transduction efficiency. We showed that TransPhage successfully transduced the human cells with an excellent efficiency (up to 95%) comparable to or superior to that of the adeno-associated virus vectors. Moreover, we showed that TransPhage’s tropism was specific to the cells that overexpress the target antigen, whereas adeno-associated viruses (AAVs) promiscuously infected many cell types. Using TransPhage as a gene therapy vehicle, we invented an NK-cell-mediated immunotherapy in which a membrane-bound fragment crystallizable region was introduced to cancer cells. We showed in vitro that the cancer cells expressing the membrane-bound fragment crystallizable (Fc) were effectively killed by CD16+ NK cells through an antibody-dependent cell-mediated cytotoxicity (ADCC)-like mechanism. In the xenograft mouse model, the administration of TransPhage carrying the membrane-bound Fc gene greatly suppressed tumor growth.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".