A Novel Quantitative Fluorescent Reporter Assay for RAG Targets and RAG Activity
Bibliographic record
Abstract
Recombination-Activating Genes (RAG) 1 and 2 form the site specific recombinase that mediates V(D)J recombination, a process of DNA editing required for lymphocyte development and responsible for their diverse repertoire of antigen receptors. Mistargeted RAG activity associates with genome alteration and is responsible for various lymphoid tumors. Moreover several non-lymphoid tumors express RAG ectopically. A practical and powerful tool to perform quantitative assessment of RAG activity and to score putative RAG-Recognition signal sequences (RSS) is required in the fields of immunology, oncology, gene therapy, and development. Here we report the detailed characterization of a novel fluorescence-based reporter of RAG activity, named GFPi, a tool that allows measuring recombination efficiency (RE) by simple flow cytometry analysis. GFPi can be produced both as a plasmid for transient transfection experiments in cell lines or as a retrovirus for stable integration in the genome, thus supporting ex vivo and in vivo studies. The GFPi assay faithfully quantified endogenous and ectopic RAG activity as tested in genetically modified fibroblasts, tumor derived cell lines, developing pre-B cells, and hematopoietic cells. The GFPi assay also successfully ranked the RE of various RSS pairs, including bona fide RSS associated with V(D)J segments, artificial consensus sequences modified or not at specific nucleotides known to affect their efficiencies, or cryptic RSS involved in RAG-dependent activation of oncogenes. Our work validates the GFPi reporter as a practical quantitative tool for the study of RAG activity and RSS efficiencies. It should turn useful for the study of RAG-mediated V(D)J and aberrant rearrangements, lineage commitment, and vertebrate evolution.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".