Lentiviral expression system for the purification of secreted proteins from human cell cultures
Bibliographic record
Abstract
BACKGROUND: Recombinant proteins of therapeutic use are ideally produced in human cells to ensure appropriate co- and post-translational modifications. However, purification of secreted proteins from the culture media is impeded by low expression from transfected cell lines and the presence of serum proteins. Here we describe a simple and cost-effective approach based on lentiviral vector-mediated gene delivery and expression of a secreted His-tagged protein from human embryonic kidney 293 T cells and direct affinity chromatography purification from the cell culture media. RESULTS: Using a protein-based HIV entry inhibitor, soluble CD4 (sCD4), we demonstrated that 293 T cells transduced with a lentiviral vector mediated over 10-fold higher secretion of sCD4 in comparison to 293 T cells transfected with the corresponding plasmid. Secretion of sCD4 increased with the dose of the lentiviral vector up to a multiplicity of infection of 50. Exchanging the native signal peptide of sCD4 with the signal peptide of human alpha-1 antitrypsin increased expression by 50 %. There was no difference in expression from a monocistronic or bicistronic lentiviral vector. Reduction of the serum concentration in the culture media had no significant effect on the secretion of sCD4. Small-scale purification from 50 ml of culture media with reduced serum content yielded up to 1 mg of pure sCD4. Purified sCD4 bound to recombinant HIV envelope glycoprotein 120 (Env gp120) and inhibited HIV entry at concentrations comparable to published results. CONCLUSION: The procedure outlined in this study can be performed without the need for specialized reagents or equipment and could easily be adapted by any laboratory. Furthermore, the method could be used to produce sCD4 fusion proteins or other His-tagged proteins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".