TLR Ligand-Induced Type I IFNs Affect Thymopoiesis
Bibliographic record
Abstract
The interactions between TLRs and their ligands have profound immune modulation properties. Attention has focused mostly on the impact of TLR ligands on peripheral innate and adaptive immunity during viral infections, whereas little impact of TLR activation has been shown on thymic development. Here we show that treatment of murine fetal thymic organ cultures (FTOCs) with TLR3 or TLR7 ligands induced rapid expression of IFN-alpha and -beta mRNA, hallmarks of acute and chronic viral infections. This resulted in an early developmental blockade, increased frequencies of apoptotic cells, and decreased proliferation of thymocytes, which led to an immediate decrease in cellularity. FTOCs infected with vesicular stomatitis virus, known to act through TLR7, were similarly affected. Down-regulation of IL-7R alpha-chain expression, together with an increased expression of suppressor of cytokine signaling-1 and a concomitant decreased expression of the transcriptional regulator growth factor independence 1 were observed in TLR ligands or IFN-treated FTOCs. This indicates a role for these pathways in the observed changes in thymocyte development. Taken together, our data demonstrate that TLR activation and ensuing type I IFN production exert a deleterious effect on T cell development. Because TLR ligands are widely used as vaccine adjuvants, their immunomodulatory actions mediated mainly by IFN-alpha suggested by our results should be taken in consideration.
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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".