BLOCKING IL-7 SIGNALING PREVENTS THE DEVELOPMENT OF LUPUS-LIKE AUTOIMMUNITY IN MICE BY RESTRICTING T CELL EXPANSION, ACTIVATION, AND COMMUNICATION WITH STROMAL CELLS
Bibliographic record
Abstract
PV065 / #555 Poster Topic: AS07 - Cutaneous Lupus Background/Purpose Our lab previously discovered that VGLL3, a female biased proinflammatory transcription factor, can drive lupus-like systemic inflammation when overexpressed under the K5 promoter in mouse epidermis.[1] IL-7 signaling is more active in the skin of lupus patients vs. healthy donors and in our K5-Vgll3 mice vs WT controls. Deletion of peripheral Il7r in K5-Vgll3 mice prevents the development of disease phenotype, with amelioration of dermatitis, splenomegaly, lymphadenopathy, and kidney damage. Methods Single-cell RNA sequencing with in-depth analyses was performed to assess cell-specific transcriptomic changes, cell-cell interactions, and molecular pathways shifted by the Il7r deletion in skin of K5-Vgll3 mice (n=3 mice per genotype). Results K5-Vgll3 mice exhibited a striking expansion of T cells in skin compared to WT , and Il7r deletion drastically reduced CD8, Tfh-like, and MAIT cells. Expression of Cd69 and Il2ra activation markers were reduced across T cell subpopulations. Marked expansion of pro-fibrotic Tnc+ fibroblasts in K5-Vgll3 mice was entirely eliminated by Il7r deletion (Figure 1). Il7r deletion also restored the proportion of differentiated and keratinized keratinocytes to WT levels. Ligand-receptor analyses determined that deleting Il7r significantly reduced lupus-associated cell-cell interactions, with the highest reduction being in the communication between Cxcl12 fibroblasts and Tfh-like T cells. Pathway analysis identified Beta1 integrin and Syndecan-1 signaling in fibroblasts and Il-12, Il-23, and TCR signaling in T cells (Figure 2) as the most affected by Il7r deletion. Figure 1. Figure 2. Conclusions VGLL3-driven lupus-like autoimmunity is dependent on IL-7 signaling and involves expansion and activation of several T cell subtypes and changes in stromal composition, most notably an increase in Tnn fibroblasts. Restricting IL-7 signaling by deleting Il7r successfully shifts transcriptomic changes and cell-cell interactions toward a healthy state, which underscores the potential of its targetability for therapeutic intervention in lupus patients. References: [1.] Billi AC. JCI Insight 2019;Apr18;4(8):e127291.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 0.002 |
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".