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.
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 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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".