Engineering the oxygen sensing regulation results in an enhanced recombinant human hemoglobin production by <i>Saccharomyces cerevisiae</i>
Bibliographic record
Abstract
Efficient production of appropriate oxygen carriers for transfusions (blood substitutes or artificial blood) has been pursued for many decades, and to date several strategies have been used, from synthetic polymers to cell-free hemoglobin carriers. The recent advances in the field of metabolic engineering also allowed the generation of different genetically modified organisms for the production of recombinant human hemoglobin. Several studies have showed very promising results using the bacterium Escherichia coli as a production platform, reporting hemoglobin titers above 5% of the total cell protein content. However, there are still certain limitations regarding the protein stability and functionality of the recombinant hemoglobin produced in bacterial systems. In order to overcome these limitations, yeast systems have been proposed as the eukaryal alternative. We recently reported the generation of a set of plasmids to produce functional human hemoglobin in Saccharomyces cerevisiae, with final titers of active hemoglobin exceeding 4% of the total cell protein. In this study, we propose a strategy for further engineering S. cerevisiae by altering the oxygen sensing pathway by deleting the transcription factor HAP1, which resulted in an increase of the final recombinant active hemoglobin titer exceeding 7% of the total cellular protein.
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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 | 0.000 |
| 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 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".