Differential Leukocyte MicroRNA Responses Following Pan T Cell, Allorecognition and Allosecretome-Based Therapeutic Activation
Bibliographic record
Abstract
Abstract Effective immunomodulation of T-cell responses is critical in treating both autoimmune diseases and cancer. Our previous studies have demonstrated that secretomes derived from control or methoxypolyethylene glycol mixed lymphocyte alloactivation assays exerted potent immunomodulatory activity that was mediated by microRNAs (miRNA). The immunomodulatory effects of biomanufactured miRNA-based allo-secretome therapeutics (SYN, TA1, IA1 and IA2) were compared to Pan T-cell activators (PHA and anti-CD3/CD28) and lymphocyte alloactivation. The differential effects of these activation strategies on resting peripheral blood mononuclear cells (PBMC) were assessed via T-cell proliferation, subset analysis and miRNA expression profiles. Mitogen-induced PBMC proliferation (> 85%) significantly exceeded that arising from either allostimulation (~ 30%) or the pro-inflammatory IA1 secretome product (~ 12%). Consequent to stimulation, the ratio of CD4 to CD8 cells of the resting PBMC (CD4:CD8; 1.7 ± 0.1) decreased in the Pan T cell, allrecognition and IA1 activated cells (averages of 1.1 ± 0.2; 1.2 ± 0.1 and 1.0 ± 0.1). These changes arose consequent to the expansion of both CD4+CD8+ and CD4–CD8– populations as well as the shrinkage of the CD4 subset and the expansion of the CD8 T cells. Importantly, these activation strategies induced vastly different miRNA expression profiles which were associated with significant differences in cellular differentiation and biological function. These findings support the concept that the “differential patterns of miRNA expression” regulate the biologic immune response in a “lock and key” manner. The biomanufacturing of miRNA-enriched secretome biotherapeutics may be a successful therapeutic approach for the systemic treatment of autoimmune diseases (TA1) and cancer (IA1).
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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".