Optimizing human α-galactosidase for treatment of Fabry disease
Bibliographic record
Abstract
Abstract Fabry disease is caused by a deficiency of α-galactosidase A (GLA) leading to the lysosomal accumulation of globotriaosylceramide (Gb3) and other glycosphingolipids. Fabry patients experience significant damage to the heart, kidney, and blood vessels that can be fatal. Here we apply directed evolution to generate more stable GLA variants as potential next generation treatments for Fabry disease. GLAv05 and GLAv09 were identified after screening more than 12,000 GLA variants through 8 rounds of directed evolution. Both GLAv05 and GLAv09 exhibit increased stability at both lysosomal and blood pH, stability to serum, and elevated enzyme activity in treated Fabry fibroblasts (19-fold) and GLA–/–podocytes (10-fold). GLAv05 and GLAv09 show improved pharmacokinetics in mouse and non-human primates. In a Fabry mouse model, the optimized variants showed prolonged half-lives in serum and relevant tissues, and a decrease of accumulated Gb3 in heart and kidney. To explore the possibility of diminishing the immunogenic potential of rhGLA, amino acid residues in sequences predicted to bind MHC II were targeted in late rounds of GLAv09 directed evolution. An MHC II-associated peptide proteomics assay confirmed a reduction in displayed peptides for GLAv09. Collectively, our findings highlight the promise of using directed evolution to generate enzyme variants for more effective treatment of lysosomal storage diseases.
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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) | 0.001 | 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".