Matrix stiffness-dependent regulation of immunomodulatory genes in human MSCs is associated with the lncRNA CYTOR
Bibliographic record
Abstract
Cell-matrix interactions in 3D environments significantly differ from those in 2D cultures. As such, mechanisms of mechanotransduction in 2D cultures are not necessarily applicable to cell-encapsulating hydrogels that resemble features of tissue architecture. Accordingly, the characterization of molecular pathways in 3D matrices is expected to uncover insights into how cells respond to their mechanical environment in physiological contexts, and potentially also inform hydrogel-based strategies in cell therapies. In this study, a bone marrow-mimetic hydrogel was employed to systematically investigate the stiffness-responsive transcriptome of mesenchymal stromal cells. High matrix rigidity impeded integrin-collagen adhesion, resulting in changes in cell morphology characterized by a contractile network of actin proximal to the cell membrane. This resulted in a suppression of extracellular matrix-regulatory genes involved in the remodeling of collagen fibrils, as well as the upregulation of secreted immunomodulatory factors. Moreover, an investigation of long noncoding RNAs revealed that the cytoskeleton regulator RNA (CYTOR) contributes to these 3D stiffness-driven changes in gene expression. Knockdown of CYTOR using antisense oligonucleotides enhanced the expression of numerous mechanoresponsive cytokines and chemokines to levels exceeding those achievable by modulating matrix stiffness alone. Taken together, our findings further our understanding of mechanisms of mechanotransduction that are distinct from canonical mechanotransductive pathways observed in 2D cultures.
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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".