Hepatocyte growth factor stimulates adenoviral‐mediated gene transfer across the apical membrane of epithelial cells
Bibliographic record
Abstract
BACKGROUND: The apical surface of polarized epithelial cells is relatively resistant to gene delivery by various agents including adenoviral vectors. Hepatocyte growth factor (HGF) dedifferentiates previously well-polarized Madin-Darby canine kidney (MDCK) cell monolayers by altering cell-surface polarity and inhibiting tight junction function. METHODS: We used an in vitro model of polarized MDCK cells grown on permeable supports to examine the effects of HGF pretreatment on adenoviral (Ad)-mediated gene delivery through the apical surface of epithelial cell monolayers. RESULTS: HGF pretreatment of MDCK cell monolayers for 72 h increased Ad-mediated gene transfer and expression of enhanced green fluorescent protein (EGFP) and luciferase in a dose-dependent fashion. Time-course analysis of HGF-induced stimulation of Ad-mediated gene transfer was seen after 24 h and increased further with pretreatment periods extending to 72 h. HGF pretreatment increased Ad-mediated gene transfer at varying multiplicity of infection (MOI; ranging from 0.2-2000). PCR analysis for adenoviral DNA in control and HGF-pretreated MDCK cells suggested increased entry of viral constructs into HGF-pretreated MDCK cell monolayers. HGF-induced alterations in cell polarity are reversible upon removal of HGF. CONCLUSIONS: These data demonstrate that HGF pretreatment of MDCK cells increases the sensitivity of the cells to Ad-mediated gene delivery. The mechanism by which this occurs appears to be through increased entry of adenovirus into epithelial cells. These data provide evidence that biological agents that transiently alter epithelial cell polarity and tight junction function can be used to augment Ad-mediated gene delivery into epithelial cells from the apical surface.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".