<i>In vivo</i>synthesis and secretion of erythropoietin by genetically modified primary human keratinocytes grafted onto immunocompromised mice
Bibliographic record
Abstract
BACKGROUND: The skin is an easily accessible tissue with a high blood flow facilitating the distribution of secreted peptides. These features make it a very intriguing target to serve as a biofactory releasing a systemically needed factor, such as erythropoietin (EPO). METHODS: To evaluate the potential of human keratinocytes (KC) to systemically synthesize EPO, EPO-transduced KC were grafted onto immunocompromised mice and EPO secretion was followed by serum ELISA. Furthermore, we assessed if topical colchicine application would select for enriched percentages of KC expressing the multi-drug resistance (MDR) gene as a selectable gene connected to the EPO gene (measured by fluorescence-activated cell sorting (FACS)-analysis) and result in enhanced EPO production (determined by ELISA). RESULTS: Transduced KC showed stable EPO production in vivo during a 6-month observation period, pointing to engraftment of EPO-secreting KC progenitor cells. When adding colchicines the number of EPO/MDR+ KC were significantly enriched, both in skin grafts (in vivo) and in skin equivalents (in vitro). Of note, this did not result in enhanced EPO production. Rather, while EPO secretion was substantially increased in transduced KC grown as monolayers and selected with colchicine, it was reduced by more than 50% in both colchicine-treated skin grafts and skin equivalents. CONCLUSION: Keratinocytes carry the potential to serve as a genetically modified biofactory synthesizing human EPO. In vivo gene selection does not allow to select for increased EPO secretion, most likely because of altered secretory activity of transduced KC in the stratified, differentiated epidermis. Thus, further studies are necessary to optimize the release of EPO by genetically modified KC.
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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 | 0.001 | 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".