Microbiota-Derived Compounds Drive Steady-State Granulopoiesis via MyD88/TICAM Signaling
Bibliographic record
Abstract
Neutropenia is probably the strongest known predisposition to infection with otherwise harmless environmental or microbiota-derived species. Because initial swarming of neutrophils at the site of infection occurs within minutes, rather than the hours required to induce "emergency granulopoiesis," the relevance of having high numbers of these cells available at any one time is obvious. We observed that germ-free (GF) animals show delayed clearance of an apathogenic bacterium after systemic challenge. In this article, we show that the size of the bone marrow myeloid cell pool correlates strongly with the complexity of the intestinal microbiota. The effect of colonization can be recapitulated by transferring sterile heat-treated serum from colonized mice into GF wild-type mice. TLR signaling was essential for microbiota-driven myelopoiesis, as microbiota colonization or transferring serum from colonized animals had no effect in GF MyD88(-/-)TICAM1(-/-) mice. Amplification of myelopoiesis occurred in the absence of microbiota-specific IgG production. Thus, very low concentrations of microbial Ags and TLR ligands, well below the threshold required for induction of adaptive immunity, sets the bone marrow myeloid cell pool size. Coevolution of mammals with their microbiota has probably led to a reliance on microbiota-derived signals to provide tonic stimulation to the systemic innate immune system and to maintain vigilance to infection. This suggests that microbiota changes observed in dysbiosis, obesity, or antibiotic therapy may affect the cross talk between hematopoiesis and the microbiota, potentially exacerbating inflammatory or infectious states in the host.
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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".