Characterization of the RSL1‐dependent conditional expression system in LNCaP prostate cancer cells and development of a single vector format
Bibliographic record
Abstract
BACKGROUND: Conditional expression systems are useful tools for the study of gene function but the use of these systems in prostate cancer cells is limited by the undesired biological effects of the inducing ligands. The RheoSwitch system employs RheoSwitch Ligand 1 (RSL1), a non-steroidal analog of the insect hormone ecdysone, to activate a modified nuclear receptor heterodimer that controls target gene expression via GAL4 response elements. This system has not been tested in prostate cancer cells. METHODS: We established LNCaP human prostate cancer cell lines that constitutively express RheoSwitch transcription factors to quantify RSL1-dependent expression and assess the effects of RSL1 on cell proliferation and endogenous gene expression. Potential RSL1-responsive genes were identified using Affymetrix microarrays and validated by Northern blot hybridization. A single-vector format was developed to establish cell lines that conditionally produce a recombinant protein. RESULTS: Stable cell lines displayed tight and potent (over several orders of magnitude) RSL1-dependent regulation of a transiently transfected luciferase reporter gene. RSL1 did not affect basal or androgen-stimulated cell proliferation and exerted minimal effects on the expression of endogenous genes. Cell lines established using the single-vector system also displayed strictly RSL1-dependent production of the recombinant protein encoded by the stably integrated RSL1-responsive expression cassette. CONCLUSIONS: The RheoSwitch system is well suited for conditional gene expression in prostate cancer cells. The single-vector format should facilitate the production of stable cell lines. This system should be useful for the study of proteins involved in prostate cancer in both cell and animal models of the disease.
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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) | 0.000 | 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) | 0.000 | 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".