Transient immunosuppression blocks iBALT formation in the lung induced by helper-dependent adenoviral vector during gene delivery
Bibliographic record
Abstract
Abstract Lung is an immune sensitive organ with presence of all immune cells at the frontline of immunity. Under chronic infection conditions, inducible Bronchus-associated lymphoid tissue (iBALT) can be formed to defend foreign infectious organisms. We wonder whether viral vectors for gene therapy could induce iBALT formation and whether this process can be controlled through immune modulation. To test these ideas, we repeatedly administered a helper-dependent adenoviral (HD-Ad) vector to mouse lungs and examined iBALT formation. HD-Ad vectors are based on adenovirus but with all viral coding sequences deleted and is one option for large gene delivery. In our previous studies, HD-Ad vectors have exhibited high in vivo efficiency in transduction of mouse, rabbit and pig airway epithelial cells, including stem/progenitor cells such as basal cells. HD-Ad vectors expressing therapeutic gene encoding cystic fibrosis transmenbrane conductance regulator (CFTR) rescue CFTR function in CF patient primary cells. In our mouse studies, we found that HD-Ad vectors induced iBALT formation and antibody production. We used cyclophosphamide or HD-Ad expressing IL-10 to modulate the host immune responses and to examine the effects on iBALT formation. Comparing with control group (HD-Ad vectors only), the mice treated with cyclophosphamide or IL-10 expressing HD-Ad vector showed a significant reduction of iBALT formation and infiltration of inflammatory cells, including CD4+ and B220+ as well as production of anti-Ad antibodies and neutralizing antibodies. Our results indicate that transient modulation significantly mitigated immunopathology and extended transgene expression in mouse airways following readministration of HD-Ad vectors.
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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) | 0.001 | 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".