A novel purification strategy for retrovirus gene therapy vectors using heparin affinity chromatography
Bibliographic record
Abstract
Membrane separation and chromatographic technologies are regarded as an attractive alternative to conventional academic small-scale ultracentrifugation procedures used for retrovirus purification. However, despite the increasing demands for purified retroviral vector preparations, new chromatography adsorbents with high specificity for the virus have not been reported. Heparin affinity chromatography is presented here as a novel convenient tool for retrovirus purification. The ability of bioactive retroviral particles to specifically bind to heparin ligands immobilized on a chromatographic gel is shown. A purification factor of 63 with a recovery of 61% of functional retroparticles was achieved using this single step. Tentacle heparin affinity supports captured retroviral particles more efficiently than conventional heparin affinity chromatography supports with which a lower recovery was obtained (18%). Intact, infective retroviral particles were recovered by elution with low salt concentrations (350 mM NaCl). Mild conditions for retrovirus elution from chromatographic columns are required to preserve virus infectivity. VSV-G pseudotyped retroviruses have shown to be very sensitive to high ionic strength, losing 50% of their activity and showing membrane damage after a short exposure to 1M NaCl. We also report a complete scaleable downstream processing scheme for the purification of MoMLV-derived vectors that involves sequential microfiltration and ultra/diafiltration steps for virus clarification and concentration respectively, followed by fractionation by heparin affinity chromatography and final polishing by size-exclusion chromatography. Overall, by using this strategy, a 38% yield of infective particles can be achieved with a final purification factor of 2,000.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".