GapmeR cellular internalization by macropinocytosis induces sequence-specific gene silencing in human primary T-cells
Bibliographic record
Abstract
Post-transcriptional gene silencing holds great promise in discovery research for addressing intricate biological questions and as therapeutics. While various gene silencing approaches, such as siRNA and CRISPR-Cas9 techniques, are available, these cannot be effectively applied to "hard-to-transfect" primary T-lymphocytes. The locked nucleic acid-conjugated chimeric antisense oligonucleotide, called "GapmeR", is an emerging new class of gene silencing molecule. Here, we show that GapmeR internalizes into human primary T-cells through macropinocytosis. Internalized GapmeR molecules can associate with SNX5-positive macropinosomes in T-cells, as detected by super-resolution microscopy. Utilizing the intrinsic self-internalizing capability of GapmeR, we demonstrate significant and specific depletion (>70%) of the expression of 5 different endogenous proteins with varying molecular weights (18 kDa Stathmin, 80 kDa PKCε, 180 kDa CD11a, 220 kDa Talin1 and 450 kDa CG-NAP/AKAP450) in human primary and cultured T-cells. Further functional analysis confirms CG-NAP and Stathmin as regulators of T-cell motility. Thus, in addition to screening, identifying or verifying critical roles of various proteins in T-cell functioning, this study provides novel opportunities to silence individual or multiple genes in a subset of purified human primary T-cells that would be exploited as future therapeutics.
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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 | 0.001 | 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) | 0.001 | 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".