Differential regulation of lipopolysaccharide and Gram‐positive bacteria induced cytokine and chemokine production in macrophages by Gα<sub>i</sub> proteins
Bibliographic record
Abstract
Heterotrimeric G(i) proteins play a role in signalling activated by lipopolysaccharide (LPS), Staphylococcus aureus (SA) and group B streptococci (GBS), leading to production of inflammatory mediators. We hypothesized that genetic deletion of G(i) proteins would alter cytokine and chemokine production induced by LPS, SA and GBS stimulation. LPS-induced, heat-killed SA-induced and heat-killed GBS-induced cytokine and chemokine production in peritoneal macrophages from wild-type (WT), Galpha(i2) (-/-) or Galpha(i1/3) (-/-) mice were investigated. LPS induced production of tumour necrosis factor-alpha (TNF-alpha), interleukin-6 (IL-6), IL-10 and interferon-gamma-inducible protein-10 (IP-10); SA induced TNF-alpha, and IL-1beta production; and GBS induced TNF-alpha, IL-6, IL-1beta, macrophage inflammatory protein-1alpha (MIP-1alpha) and keratinocyte chemoattract (KC) production were all decreased (P < 0.05) in Galpha(i2) (-/-) or Galpha(i1/3) (-/-) mice compared with WT mice. In contrast to the role of G(i) proteins as a positive regulator of mediators, LPS-induced production of MIP-1alpha and granulocyte-macrophage colony-stimulating factor (GM-CSF) were increased in macrophages from Galpha(i1/3) (-/-) mice, and SA-induced MIP-1alpha production was increased in both groups of Galpha(i) protein-depleted mice. LPS-induced production of KC and IL-1beta, SA-induced production of GM-CSF, KC and IP-10, and GBS-induced production of IL-10, GM-CSF and IP-10 were unchanged in macrophages from Galpha(i2) (-/-) or Galpha(i1/3) (-/-) mice compared with WT mice. These data suggest that G(i2) and G(i1/3) proteins are both involved and differentially regulate murine inflammatory cytokine and chemokine production in response to both LPS and Gram-positive microbial stimuli.
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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 | 0.000 |
| 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".