Faculty Opinions recommendation of Recognition of peptidoglycan from the microbiota by Nod1 enhances systemic innate immunity.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Humans are colonized by a large bacterial flora (the microbiota) essential for the development of the gut immune system [1][2][3] .A broader role for the microbiota as a major modulator of systemic immunity has been proposed 4,5 , however, evidence and mechanism have remained elusive.We show that the microbiota is a source of peptidoglycan that systemically primes the innate immune system, enhancing killing by bone marrow-derived neutrophils of two important pathogens: Streptococcus pneumoniae and Staphylococcus aureus.This requires signaling via the pattern recognition receptor Nod1 (which recognizes mesoDAP-containing peptidoglycan found predominantly in Gram-negative bacteria), but not Nod2 (which detects peptidoglycan found in Gram-positive and Gram-negative bacteria) or Tlr4 (which recognizes lipopolysaccharide) 6,7 .We demonstrate translocation of peptidoglycan from the gut to neutrophils in the bone marrow and show levels in sera correlate with neutrophil function.In vivo administration of Nod1 ligands is sufficient to restore neutrophil function after microbiota depletion.Nod1 -/-mice show increased susceptibility to early pneumococcal sepsis, demonstrating a role for Nod1 in priming innate defenses facilitating a rapid response to infection.These data establish a mechanism for systemic immunomodulation by the microbiota and highlight potential adverse consequences of microbiota disruption, by broad-spectrum antibiotics, on innate immune defense to infection.Humans are colonized by approximately 10 13 -10 14 bacteria, residing primarily on the mucosal surfaces of the host 8 .Understanding host-bacteria relationships has generally Users may view, print, copy,
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it