Matrix protein Tenascin-C expands and reversibly blocks maturation of eosinophil progenitors
Bibliographic record
Abstract
Abstract While eosinophil depletion is a sought after strategy in treating allergic disease, little is known about the local tissue factors regulating eosinophils. In disease, eosinophils encounter provisional extracellular matrix (ECM) glycoproteins such as Tenascin-C (TNC), which strongly correlates with eosinophil expansion in tissues. While restricted in healthy adult lungs, TNC is a hematopoietic niche component in bone marrow stroma and is expressed de novo during wound healing or in pathologic epithelial remodeling in asthma. RNA-Seq shows that exposing naïve murine eosinophils to TNC upregulates immaturity markers (CD34, CD117, and Sca-1) and suppresses IL-5Rα. Thus, we hypothesized that TNC represents a factor in the provisional allergic tissue microenvironment that could support in situ eosinophil progenitor expansion. In murine bone marrow-derived cultures, TNC: 1) 3-fold expanded the available Lin−Sca1+ early precursor pool; 2) downregulated IL-5Rα expression on Lin− CD45+ c-kit+ cells during the eosinophil lineage commitment phase; 3) served as a reversible eosinophil maturation block, as TNC withdrawal rescued maturation and increased final eosinophil yield. Moreover, TNC knockout mice lack in situ lung expansion of Lin− c-kit+ CD34+ common myeloid progenitors and Lin− Siglec-F+ Sca-1+ cells in an allergic inflammation model as well as exhibit accelerated lung eosinophil maturation ex vivo. Adding TNC to lung homogenates cultured in IL-5 ex vivo suppressed eosinophil maturation in a manner consistent with TNC block of eosinophil maturation in bone marrow-derived cultures. Together, our results stress the local tissue factor potential to promote in situ expansion and eosinophil persistence in allergic disease.
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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 | 0.001 |
| 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".