LSD1 Inhibition Enhances the Immunogenicity of Mesenchymal Stromal Cells by Eliciting a dsRNA Stress Response
Bibliographic record
Abstract
Mesenchymal stromal cells (MSCs) are commonly known for their immune-suppressive abilities. However, our group provided evidence that it is possible to convert MSCs into potent antigen presenting cells (APCs) using either genetic engineering or pharmacological means. Given the capacity of UM171a to trigger APC-like function in MSCs, and the recent finding that this drug may modulate the epigenome by inhibiting the lysine-specific demethylase 1 (LSD1), we explored whether the direct pharmacological inhibition of LSD1 could instill APC-like functions in MSCs akin to UM171a. The treatment of MSCs with the LSD1 inhibitor tranylcypromine (TC) elicits a double-stranded (ds)RNA stress response along with its associated responsive elements, including pattern recognition receptors (PRRs), Type-I interferon (IFN), and IFN-stimulated genes (ISGs). The net outcome culminates in the enhanced expression of H2-Kb, and an increased stability of the cell surface peptide: MHCI complexes. As a result, TC-treated MSCs stimulate CD8 T-cell activation efficiently, and elicit potent anti-tumoral responses against the EG.7 T-cell lymphoma in the context of prophylactic vaccination. Altogether, our findings reveal a new pharmacological protocol whereby targeting LSD1 in MSCs elicits APC-like capabilities that could be easily exploited in the design of future MSC-based anti-cancer vaccines.
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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 | 0.000 | 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 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".