A Proinflammatory Secretome Mediates the Impaired Immunopotency of Human Mesenchymal Stromal Cells in Elderly Patients with Atherosclerosis
Bibliographic record
Abstract
Abstract Inflammation plays a pivotal role in the initiation and progression of atherosclerosis (ATH). Due to their potent immunomodulatory properties, mesenchymal stromal cells (MSCs) are evaluated as therapeutic tools in ATH and other chronic inflammatory disorders. Aging reduces MSCs immunopotency potentially limiting their therapeutic utility. The mechanisms that mediate the effect of age on MSCs immune-regulatory function remain elusive and are the focus of this study. Human adipose tissue-derived MSCs were isolated from patients undergoing coronary artery bypass graft surgery. MSCs:CD4+T-cell suppression, a readout of MSCs’ immunopotency, was assessed in allogeneic coculture systems. MSCs from elderly subjects were found to exhibit a diminished capacity to suppress the proliferation of activated T cells. Soluble factors and, to a lesser extent, direct cell-cell contact mechanisms mediated the MSCs:T-cell suppression. Elderly MSCs exhibited a pro-inflammatory secretome with increased levels of interleukin-6 (IL-6), IL-8/CXCL8, and monocyte chemoattractant protein-1 (MCP-1/CCL2). Neutralization of these factors enhanced the immunomodulatory function of elderly MSCs. In summary, our data reveal that in contrast to young MSCs, MSCs from elderly individuals with ATH secrete high levels of IL-6, IL-8/CXCL8 and MCP-1/CCL2 which mediate their reduced immunopotency. Consequently, strategies aimed at targeting pro-inflammatory cytokines/chemokines produced by MSCs could enhance the efficacy of autologous cell-based therapies in the elderly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".