Targeted knockdown of ATM, ATR, and PDEδ increases Gag HIV-1 VLP production in HEK293 cells
Bibliographic record
Abstract
Abstract Several strategies have been developed in recent years to improve virus-like particle (VLP)-based vaccine production processes. Among these, the metabolic engineering of cell lines has been one of the most promising approaches. Based on previous work and a proteomic analysis of HEK293 cells producing Human Immunodeficiency Virus-1 (HIV-1) Gag VLPs under transient transfection, four proteins susceptible of enhancing VLP production were identified: ataxia telangiectasia mutated (ATM), ataxia telangiectasia and rad3-related (ATR), DNA-dependent protein kinase catalytic subunit (DNA-PKcs), and retinal rod rhodopsin-sensitive cGMP 3',5'-cyclic phosphodiesterase subunit delta (PDEδ). The knockdown of ATM, ATR, and PDEδ in HEK293 cells increased HIV-1 VLP titers in the supernatant by 3.4-, 2.1-, and 2.2-fold, respectively. Also, possible metabolic synergies between plasmids were investigated by statistical design of experiments (DoE), enabling us to identify the optimal production strategy, that was further demonstrated at lab-scale stirred tank bioreactor operated in perfusion, significantly increasing both VLPs specific and volumetric productivities to 8.3 × 10 3 VLPs/cellxday and 7.5 × 10 12 VLPs/Lxday, respectively. Key points • ATM, ATR, and PDEδ knockdowns increased VLP production in HEK293 cells. • Knockdown of ATM increased budding efficiency and extracellular vesicle concentration. • ATM knockdown could be intensified to bioreactor scale operated in perfusion. Graphical Abstract
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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 | 0.001 |
| 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".