Specific inflammatory stimuli that engage innate immune sensors induce novel CD103 expression profiles in macrophages
Bibliographic record
Abstract
The integrin CD103 is an adhesion molecule that facilitates immune cell retention in epithelial tissues through its interaction with E-cadherin. It is a marker for certain CD8+ T-cell subpopulations and conventional type 1 dendritic cells (cDC1), but its presence on macrophages remains poorly characterized. Macrophage differentiation is influenced by M-CSF and GM-CSF, and we investigated whether macrophages can also express CD103 under inflammatory conditions. We examined baseline CD103 expression in bone marrow-derived macrophages (BMDMs) differentiated in M-CSF or GM-CSF and then stimulated them with pathogen-associated molecular patterns (PAMPs) or examined them following viral infection. We found that CD103 is minimally expressed at baseline but is selectively upregulated in M-CSF-differentiated macrophages after stimulation with endosomal TLR agonists. Mechanistically, p38 MAPK inhibition prevented CD103 upregulation, suggesting that this process is mediated by p38 MAPK signaling. Furthermore, in vivo LCMV infection induced CD103 expression on peritoneal macrophages. These findings demonstrate that macrophages can express CD103 under specific inflammatory conditions, challenging the assumption that CD103 is restricted to T cells and dendritic cells. This study expands our understanding of CD103 beyond its recognized roles in T cells and DCs, providing new insight into its regulation by macrophages.
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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".