Cerebral Microglia Recruit Monocytes into the Brain in Response to Tumor Necrosis Factorα Signaling during Peripheral Organ Inflammation
Bibliographic record
Abstract
In inflammatory diseases occurring outside the CNS, communication between the periphery and the brain via humoral and/or neural routes results in central neural changes and associated behavioral alterations. We have recently identified another immune-to-CNS communication pathway in the setting of organ-centered peripheral inflammation: namely, the entrance of immune cells into the brain. In our current study, using a mouse model of inflammatory liver injury, we have confirmed the significant infiltration of activated monocytes into the brain in mice with hepatic inflammation and have defined the mechanism that mediates this trafficking of monocytes. Specifically, we show that in the presence of hepatic inflammation, mice demonstrate elevated cerebral monocyte chemoattractant protein (MCP)-1 levels, as well as increased numbers of circulating CCR2-expressing monocytes. Cerebral recruitment of monocytes was abolished in inflamed mice that lacked MCP-1/CCL2 or CCR2. Furthermore, in mice with hepatic inflammation, microglia were activated and produced MCP-1/CCL2 before cerebral monocyte infiltration. Moreover, peripheral tumor necrosis factor (TNF)alpha signaling was required to stimulate microglia to produce MCP-1/CCL2. TNFalpha signaling via TNF receptor 1 (TNFR1) is required for these observed effects since in TNFR1 deficient mice with hepatic inflammation, microglial expression of MCP-1/CCL2 and cerebral monocyte recruitment were both markedly inhibited, whereas there was no inhibition in TNFR2 deficient mice. Our results identify the existence of a novel immune-to-CNS communication pathway occurring in the setting of peripheral organ-centered inflammation which may have specific implications for the development of alterations in cerebral neurotransmission commonly encountered in numerous inflammatory diseases occurring outside 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 | 0.001 | 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".