Fanconi Anemia Mesenchymal Stromal Cells-Derived Glycerophospholipids Skew Hematopoietic Stem Cell Differentiation Through Toll-Like Receptor Signaling
Bibliographic record
Abstract
Fanconi anemia (FA) patients develop bone marrow (BM) failure or leukemia. One standard care for these devastating complications is hematopoietic stem cell transplantation. We identified a group of mesenchymal stromal cells (MSCs)-derived metabolites, glycerophospholipids, and their endogenous inhibitor, 5-(tetradecyloxy)-2-furoic acid (TOFA), as regulators of donor hematopoietic stem and progenitor cells. We provided two pieces of evidence that TOFA could improve hematopoiesis-supporting function of FA MSCs: (a) limiting-dilution cobblestone area-forming cell assay revealed that TOFA significantly increased cobblestone colonies in Fanca-/- or Fancd2-/- cocultures compared to untreated cocultures. (b) Competitive repopulating assay using output cells collected from cocultures showed that TOFA greatly alleviated the abnormal expansion of the donor myeloid (CD45.2+Gr1+Mac1+) compartment in both peripheral blood and BM of recipient mice transplanted with cells from Fanca-/- or Fancd2-/- cocultures. Furthermore, mechanistic studies identified Tlr4 signaling as the responsible pathway mediating the effect of glycerophospholipids. Thus, targeting glycerophospholipid biosynthesis in FA MSCs could be a therapeutic strategy to improve hematopoiesis and stem cell transplantation.
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) | 0.002 | 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".