G-CSF-mediated inhibition of JNK is a key mechanism for Lactobacillus rhamnosus-induced suppression of TNF production in macrophages
Bibliographic record
Abstract
Lactobacillus rhamnosus is a human commensal with known immunomodulatory properties. To date the mechanism of these immunomodulatory effects is not well understood. To unravel the immunomodulatory signalling mechanism, we investigated the effects of two strains of L. rhamnosus, L. rhamnosus GG and GR-1, in modulating production of tumour necrosis factor-alpha (TNF) in human monocytic cell line THP-1 and mouse macrophages. Live L. rhamnosus GG and GR-1 or their spent culture supernatant induced minuscule amounts of TNF production but large quantities of granulocyte-colony stimulating factor (G-CSF) in macrophages compared with those induced by pathogenic Escherichia coli GR-12 and Enterococcus faecalis. By using neutralizing antibodies and G-CSF receptor knockout mice, we demonstrated that G-CSF secreted from L. rhamnosus GG- and GR-1-exposed macrophages suppressed TNF production induced by E. coli- or lipopolysaccharide-activated macrophages through a paracrine route. The suppression of TNF production by G-CSF was mediated through activation of STAT3 and subsequent inhibition of c-Jun-N-terminal kinases (JNKs). The inhibition of JNK activation required STAT3alpha-mediated de novo protein synthesis. This demonstrates a novel role of G-CSF in L. rhamnosus-triggered anti-inflammatory effects and its mechanism in the suppression of TNF production in macrophages.
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 | 0.000 |
| 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".