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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".