A novel expression platform for the production of diabetes-associated autoantigen human glutamic acid decarboxylase (hGAD65)
Bibliographic record
Abstract
BACKGROUND: Human glutamic acid decarboxylase 65 (hGAD65) is a key autoantigen in type 1 diabetes, having much potential as an important marker for the prediction and diagnosis of type 1 diabetes, and for the development of novel antigen-specific therapies for the treatment of type 1 diabetes. However, recombinant production of hGAD65 using conventional bacterial or mammalian cell culture-based expression systems or nuclear transformed plants is limited by low yield and low efficiency. Chloroplast transformation of the unicellular eukaryotic alga Chlamydomonas reinhardtii may offer a potential solution. RESULTS: A DNA cassette encoding full-length hGAD65, under the control of the C. reinhardtii chloroplast rbcL promoter and 5'- and 3'-UTRs, was constructed and introduced into the chloroplast genome of C. reinhardtii by particle bombardment. Integration of hGAD65 DNA into the algal chloroplast genome was confirmed by PCR. Transcriptional expression of hGAD65 was demonstrated by RT-PCR. Immunoblotting verified the expression and accumulation of the recombinant protein. The antigenicity of algal-derived hGAD65 was demonstrated with its immunoreactivity to diabetic sera by ELISA and by its ability to induce proliferation of spleen cells from NOD mice. Recombinant hGAD65 accumulated in transgenic algae, accounts for approximately 0.25-0.3% of its total soluble protein. CONCLUSION: Our results demonstrate the potential value of C. reinhardtii chloroplasts as a novel platform for rapid mass production of immunologically active hGAD65. This demonstration opens the future possibility for using algal chloroplasts as novel bioreactors for the production of many other biologically active mammalian therapeutic proteins.
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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) | 0.001 | 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 | 0.001 |
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".