IFN-γ-Induced Chemokines Synergize with Pertussis Toxin to Promote T Cell Entry to the Central Nervous System
Bibliographic record
Abstract
Inflammation of the CNS, which occurs during multiple sclerosis and experimental autoimmune encephalomyelitis, is characterized by increased levels of IFN-gamma, a cytokine not normally expressed in the CNS. To investigate the role of IFN-gamma in CNS, we used intrathecal injection of a replication-defective adenovirus encoding murine IFN-gamma (AdIFNgamma) to IFN-gamma-deficient (GKO) mice. This method resulted in stable, long-lived expression of IFN-gamma that could be detected in cerebrospinal fluid using ELISA and Luminex bead immunoassay. IFN-gamma induced expression in the CNS of message and protein for the chemokines CXCL10 and CCL5, to levels comparable to those seen during experimental autoimmune encephalomyelitis. Other chemokines (CXCL2, CCL2, CCL3) were not induced. Mice lacking the IFN-gammaR showed no response, and a control viral vector did not induce chemokine expression. Chemokine expression was predominantly localized to meningeal and ependymal cells, and was also seen in astrocytes and microglia. IFN-gamma-induced chemokine expression did not lead to inflammation. However, when pertussis toxin was given i.p. to mice infected with the IFN-gamma vector, there was a dramatic increase in the number of T lymphocytes detected in the CNS by flow cytometry. This increase in blood-derived immune cells in the CNS did not occur with pertussis toxin alone, and did not manifest as histologically detectable inflammatory pathology. These results show that IFN-gamma induces a characteristic glial chemokine response that by itself is insufficient to promote inflammation, and that IFN-gamma-induced CNS chemoattractant signals can synergize with a peripheral infectious stimulus to drive T cell entry into the CNS.
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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 | 0.000 | 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 | 0.001 |
| 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".