Uncovering miR-142 Targets Regulating B-cell Expansion in NHL
Bibliographic record
Abstract
Non-Hodgkin’s Lymphoma (NHL) is the fifth most prevalent type of cancer in Canada. Diffuse Large B-cell Lymphoma (DLBCL) accounts for 90% of aggressive NHL cases. The microRNA miR-142 is mutated in 20% of DLBCL cases. Additionally, miR-142 -/- B-cells display increased expansion and elevated B-cell Activating Factor Receptor (BAFF-R) expression. However, it remains unclear whether loss of miR-142 directly leads to elevated BAFF-R expression, leading to increased expansion. Mutating the miR-142 target site on BAFF-R and expanding B-cells could help elucidate the regulatory mechanism. If miR-142 target site KO B-cells expand more, it shows that miR-142 directly regulates BAFF-R expression. The miR-142 target site will be deleted using CRISPR. A gRNA targeting the miR-142 binding site on BAFF-R will be cloned into the PX458 plasmid, which contains the cas9 expression cassette. The resulting plasmid will then be transfected into B-cells. The transfected B-cells will then be cultured in vitro under conditions that promote B-cell expansion. The frequency of miR-142 target site mutations will be quantified via sequencing before and after expansion. If mutations are enriched in the expanded population, it would suggest that miR-142 directly regulates BAFF-R. Thus, in miR-142 -/- mice, the lack of regulation by miR-142 would be directly leading to increased levels of BAFF-R, driving increased B-cell expansion. A broader screen targeting all miR-142 targets could help uncover other critical regulators of B-cell expansion. These targets could then be studied further to develop therapeutic strategies for treating DLBCL, given that microRNAs are difficult to mimic therapeutically.
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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 | 0.001 | 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".