CRISPR-Cas Genome Editing in <i>Ex Vivo</i> Human Lungs to Rewire the Translational Path of Genome-Targeting Therapeutics
Bibliographic record
Abstract
The ongoing advancements in CRISPR-Cas technologies can significantly accelerate the preclinical development of both in vivo and ex vivo organ genome-editing therapeutics. One of the promising applications is to genetically modify donor organs prior to implantation. The implantation of optimized donor organs with long-lasting immunomodulatory capacity holds promise for reducing the need for lifelong potent whole-body immunosuppression in recipients. However, assessing genome-targeting interventions in a clinically relevant manner prior to clinical trials remains a major challenge owing to the limited modalities available. This study introduces a novel platform for testing genome editing in human lungs ex vivo , effectively simulating preimplantation genetic engineering of donor organs. We identified gene regulatory elements whose disruption via Cas nucleases led to the upregulation of the immunomodulatory gene interleukin 10 ( IL-10) . We combined this approach with adenoviral vector-mediated IL-10 delivery to create favorable kinetics for early (immediate postimplantation) graft immunomodulation. Using ex vivo organ machine perfusion and precision-cut tissue slice technology, we demonstrated the feasibility of evaluating CRISPR genome editing in human lungs. To overcome the assessment limitations in ex vivo perfused human organs, we conducted an in vivo rodent study and demonstrated both early gene induction and sustained editing of the lung. Collectively, our findings lay the groundwork for a first-in-human-organ study to overcome the current translational barriers of genome-targeting therapeutics.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".