Temporal transcriptional, metabolic, and functional re-programming of THP-1 macrophages by the <i>Lactobacillus rhamnosus</i> R0011 secretome
Bibliographic record
Abstract
Abstract Macrophage responses to activation are fluid and dynamic and their ability to respond appropriately to subsequent challenge is integral to host defence. Recent evidence suggests that bacteria influence macrophage differentiation and subsequent polarization into pro-inflammatory (M1) and immunoregulatory (M2) phenotypes through direct interactions. However, many questions surround indirect communication mechanisms mediated through secretomes derived from gut bacteria, such as lactobacilli. We examined the effects of secretome-mediated conditioning on macrophage phenotype, focusing on the ability of the Lactobacillus rhamnosus R0011 secretome (LrS) to drive macrophage polarization and to prime responses to subsequent challenge with lipopolysaccharide (LPS). Transcriptional profiling revealed increased M2-associated gene transcription (IL10R, CD36, CD163, TLR1, and TLR8) in response to LrS conditioning. Cytokine and chemokine profiling confirmed these results, indicating increased M2-associated chemokine and cytokine production (IL-10, CCL1, 17, 20, CXCL1 and 2). Metabolite utilization assays indicated diminished reliance on glycolysis for energy generation, coupled with increased phagocytic capacity, characteristics of functional M2 activity. LPS challenge of LrS-conditioned THP-1s revealed heightened responsiveness, indicative of innate immune priming. Overall, the LrS conditions THP-1s into M2 macrophages and primes responses to subsequent LPS challenge. Secretome conditioning of macrophages to respond robustly to inflammatory challenge within an M2 phenotype is an uncharacterized and potentially important route through which lactobacilli can train innate immunity.
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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".