Exploring neuroglial signaling: diversity of molecules implicated in microglia-to-astrocyte neuroimmune communication
Bibliographic record
Abstract
Effective communication between different cell types is essential for brain health, and dysregulation of this process leads to neuropathologies. Brain glial cells, including microglia and astrocytes, orchestrate immune defense and neuroimmune responses under pathological conditions during which interglial communication is indispensable. Our appreciation of the complexity of these processes is rapidly increasing due to recent advances in molecular biology techniques, which have identified numerous phenotypic states of both microglia and astrocytes. This review focuses on microglia-to-astrocyte communication facilitated by secreted neuroimmune modulators. The combinations of interleukin (IL)-1α, tumor necrosis factor (TNF), plus complement component C1q as well as IL-1β plus TNF are already well-established microglia-derived stimuli that induce reactive phenotypes in astrocytes. However, given the large number of inflammatory mediators secreted by microglia and the rapidly increasing number of distinct functional states recognized in astrocytes, it can be hypothesized that many more intercellular signaling molecules exist. This review identifies the following group of cytokines and gliotransmitters that, while not established as interglial mediators yet, are known to be released by microglia and elicit functional responses in astrocytes: IL-10, IL-12, IL-18, transforming growth factor (TGF)-β, interferon (IFN)-γ, C-C motif chemokine ligand (CCL)5, adenosine triphosphate (ATP), l-glutamate, and prostaglandin E2 (PGE2). The review of molecular mechanisms engaged by these mediators reveals complex, partially overlapping signaling pathways implicated in numerous neuropathologies. Additionally, lack of human-specific studies is identified as a significant knowledge gap. Further research on microglia-to-astrocyte communication is warranted, as it could discover novel interglial signaling-targeted therapies for diverse neurological disorders.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| 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".