IL-12 and Mucosal CD14+ Monocyte-Like Cells Induce IL-8 in Colonic Memory CD4+ T Cells of Patients With Ulcerative Colitis but not Crohn’s Disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: CD14+ mononuclear phagocytes [MNPs] and T cells infiltrate colon in ulcerative colitis [UC]. Here we investigated how CD14+ MNPs and the cytokines they produce shape the colonic effector T cell profile. METHODS: Colonic or mesenteric lymph node [mLNs] CD4+ T cells isolated from UC or Crohn's disease [CD] patients were stimulated with cytokines or autologous CD14+ MNPs. Cytokine expression was assessed by intracytoplasmic staining and multiplex ELISA. Unsupervised phenotypic multicolour analysis of colonic CD14+ MNPs was performed using the FlowSOM algorithm. RESULTS: Among CD14+CD64+HLA-DR+SIRPα + MNPs, only the pro-inflammatory cytokine-producing CD163- subpopulation accumulated in inflamed UC colon and promoted mucosal IL-1β-dependent Th17, Th17/Th1, Th17/Th22 but not Th1 responses. Unsupervised phenotypic analysis of CD14+CD64+ MNPs segregated CD163- monocyte-like cells and CD163+ macrophages. Unexpectedly, IL-12, IL-1β and CD163-, but not CD163+, cells induced IL-8 expression in colonic CD4+ T cells, which co-expressed IFN-γ and/or IL-17 in UC and not CD. The CD163- monocyte-like cells increased the frequency of IL-8+IL-17+/-IFN-γ +/- T cells through IL-1β and IL-12. Finally, colonic IL-8+ T cells co-expressing GM-CSF, TNF-α and IL-6 were detected ex vivo and, promoted by IL-12 in the mucosa and mLNs in UC only. CONCLUSIONS: Our findings established a link between monocyte-like CD163- MNPs, IL-12, IL-1β and the detection of colonic memory IL-8-producing CD4+ T cells, which might all contribute to the pathogenesis of UC.
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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) | 0.002 | 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".