High-Fat Diet Rapidly Modifies Trafficking, Phenotype, and Function of Plasmacytoid Dendritic Cells in Adipose Tissue
Bibliographic record
Abstract
Abstract Plasmacytoid dendritic cells (pDCs) display an increased abundance in visceral adipose tissue (VAT) of humans with obesity. In the current study, we set out to decipher the molecular mechanisms of their recruitment to VAT and the functional relevance of this process. We observed increased pDC numbers in murine blood, liver, spleen, and VAT after feeding a high-fat diet (HFD) for 3 wk when compared with a standard diet. pDCs were enriched in fat-associated lymphoid clusters representing highly specific lymphoid regions within VAT. HFD led to an enlargement of fat-associated lymphoid clusters with an increased density and migratory speed of pDCs as shown by intravital multiphoton microscopy. For their recruitment into VAT, pDCs employed P-selectin with E-selectin and L-selectin being only critical in response to HFD, indicating that the molecular cues underlying pDC trafficking were dependent on the nutritional state. Subsequent recruitment steps required α4β1 and α4β7 integrins and engagement of CCR7. Application of fingolimod (FTY720) abrogated egress of pDCs from VAT, indicating the involvement of sphingosine-1-phosphate in this process. Furthermore, HFD altered pDC functions by promoting their activation and type 1 IFN expression. Blocking pDC infiltration into VAT prevented weight gain and improved glucose tolerance during HFD. In summary, a HFD fundamentally alters pDC biology by promoting their trafficking, retention, and activation in VAT, which in turn seems to regulate metabolism.
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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 | 0.001 |
| 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".