Adherence Affects Monocyte Innate Immune Function and Metabolic Reprogramming after Lipopolysaccharide Stimulation In Vitro
Bibliographic record
Abstract
Abstract Circulating nonadherent monocytes can migrate to extravascular sites by a process that involves adherence. Alterations in intracellular metabolism shape the immunological phenotype of phagocytes upon activation. To determine the effect of adherence on their metabolic and functional response human monocytes were stimulated with LPS under nonadherent and adherent conditions. Adherent monocytes (relative to nonadherent monocytes) produced less TNF and IL-1β (proinflammatory) and more IL-10 (anti-inflammatory) upon LPS stimulation and had an increased capacity to phagocytose and produce reactive oxygen species. RNA sequencing analysis confirmed that adherence modified the LPS-induced response of monocytes, reducing expression of proinflammatory genes involved in TLR signaling and increasing induction of genes involved in pathogen elimination. Adherence resulted in an increased glycolytic response as indicated by lactate release, gene set enrichment, and [13C]-glucose flux analysis. To determine the role of glycolysis in LPS-induced immune responses, this pathway was inhibited by glucose deprivation or the glucose analogue 2-deoxy-d-glucose (2DG). Although both interventions equally inhibited glycolysis, only 2DG influenced monocyte functions, inhibiting expression of genes involved in TLR signaling and pathogen elimination, as well as cytokine release. 2DG, but not glucose deprivation, reduced expression of genes involved in oxidative phosphorylation. Inhibition of oxidative phosphorylation affected TNF and IL-10 release in a similar way as 2DG. Collectively, these data suggest that adherence may modify the metabolic and immunological profile of monocytes and that inhibition of glycolysis and oxidative phosphorylation, but not inhibition of glycolysis alone, has a profound effect on immune functions of monocytes exposed to LPS.
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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".