The role of exercise-and high fat diet-induced bone marrow extracellular vesicles in stress hematopoiesis
Bibliographic record
Abstract
Exercise and obesity regulate hematopoiesis, in part through alterations in cellular and soluble components of the bone marrow niche. Extracellular vesicles (EVs) are components of the bone marrow niche that regulate hematopoiesis; however, the role of exercise training or obesity induced EVs in regulating hematopoiesis remains unknown. To address this gap, donor EVs were isolated from control diet-fed, sedentary mice (CON-SED), control diet-fed exercise trained mice (CON-EX), high fat diet-fed, sedentary mice (HFD-SED), and high fat diet-fed, exercise trained mice (HFD-EX) and injected into recipient mice undergoing stress hematopoiesis. Hematopoietic and niche cell populations were quantified, and EV miRNA cargo was evaluated. EV content did not differ between the four groups. Mice receiving HFD-EX EVs had fewer hematopoietic stem cells (HSCs) (p < 0.01), long-term HSC (p < 0.05), multipotent progenitors (p < 0.01), common myeloid progenitors (p<0.01), common lymphoid progenitors (p < 0.01), and granulocyte-macrophage progenitors (p < 0.05), compared to mice receiving HFD-SED EVs. Similarly, mice receiving EX EVs had fewer osteoprogenitor cells compared to SED (p < 0.05) but enhanced mesenchymal stromal cell (MSC) osteogenic differentiation in vitro (p < 0.05) compared to SED EVs. HFD EVs enhanced mesenchymal stromal cell (MSC) adipogenesis in vitro (p < 0.01) compared to CON EVs. HFD-EX EVs had lower microRNA-193 and microRNA-331-5p content, microRNAs implicated in inhibiting osteogenesis and leukemic cell expansion respectively, compared to HFD-SED EVs. The results identify alterations in EV cargo as a novel mechanism by which exercise training alters stress hematopoiesis and the bone marrow niche.
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".