TLR9 modulates peritoneal immunity via regulating the biology of CD9hi fibroblastic reticular cells
Bibliographic record
Abstract
Abstract Fibroblastic reticular cells (FRC) are highly heterogeneous. Distinct FRC subsets play unique roles in the formation of secondary lymphoid organs and immune responses. We have previously shown that TLR9 signaling plays a critical role in regulating peritoneal immunity via suppressing chemokine production in FRC in the fat associated lymphoid clusters (FALC). However, the subset-specific roles of TLR9 in FRC of FALCs remain unknown. Using single-cell RNA-sequencing, we identified three distinct subsets of FRC (CD55hi, CD9hi and CD55loCD9lo) in mouse mesenteric FALCs at baseline. Based on the gene set enrichment analysis, the Cd55hi subset was enriched in gene expression related to cell differentiation. The CD9hi subset was enriched in gene expression related to immune response. The CD55loCD9lo subset was enriched in the gene expression related to extracellular matrix formation. Furthermore, we found that CD9hi FRC from Tlr9−/− mice increased gene expression associated with inflammation. Using flow cytometry and bulk-RNA seq, we successfully isolated and validated these three subsets in mouse FALCs. Interestingly, activation of TLR9 signaling using ODN1585 significantly decreased CD9 expression in FRC in vivo and in vitro. Furthermore, treatment of ODN1585 suppressed the proliferation of CD9hi FRC, evidenced by decreased expression of Ki67. These results indicate that CD9hi FRC in FALC are immunoregulatory. TLR9 signaling may modulate peritoneal immunity via regulation of the proliferation of CD9hi FRC. Understanding the mechanism of how TLR9 regulates CD9hi FRC may lead to discovering new therapies for inflammatory diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".