Compromised emergency hematopoiesis in obesity requires TLR4
Bibliographic record
Abstract
Abstract Following acute injury/infection, bone marrow (BM) precursors must responsively redirect from steady-state to emergency hematopoiesis. Prospective and retrospective studies establish obesity as risk factor for infection but the mechanisms are poorly understood. Here, we show in a murine model of obesity that moderate changes in steady-state BM hematopoiesis become exaggerated during hematopoietic duress. At steady-state, diet-induced obese mice have a 30% decrease in BM hematopoietic stem cells (HSCs) with a parallel increase in splenic HSCs. HSCs are functionally competent in vitro and following competitive transfer in vivo. BM lymphoid and myeloid progenitors in obese mice are numerically intact but the transcriptional profiles and differentiation potential are strikingly myeloid-biased, accompanied by myeloid skewing of peripheral blood. Responses to acute hematopoietic challenge are deficient. Emergency hematopoiesis following chemotherapeutic ablation is reduced 60% in obesity. Likewise, emergency myelopoiesis in response to endotoxin is characterized by accumulation of immature rather than mature myeloid cells in obese mice. The obesity-associated inflammatory environment plays a key role as transfer of obese BM to lean recipients restores emergency immune response to chemotherapeutic challenge. Moreover, mice deficient in toll-like receptor 4 (TLR4), a central regulator of inflammation, retain robust hematopoietic rebound responses despite diet-associated weight gain. Thus, in obesity, moderate changes in steady-state blood cell production mask major defects in hematopoietic stress responses, via a TLR4-dependent mechanism.
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 | 0.001 |
| 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".