Microglia induce neurogenic protein expression in primary cortical cells by stimulating PI3K/AKT intracellular signaling in vitro
Bibliographic record
Abstract
Emerging evidence suggests that microglia can support neurogenesis. Little is known about the mechanisms by which microglia regulate the cortical environment and stimulate cortical neurogenesis. We used an in vitro co-culture model system to investigate the hypothesis that microglia respond to soluble signals from cortical cells, particularly following mechanical injury, to alter the cortical environment and promote cortical cell proliferation, differentiation, and survival. Analyses of cortical cell proliferation, cell death, neurogenic protein expression, and intracellular signaling were performed on uninjured and injured cortical cells in co-culture with microglial cell lines. Microglia soluble cues enhanced cortical cell viability and proliferation cortical cells. Co-culture of injured cortical cells with microglia significantly reduced cell death of cortical cells. Microglial co-culture significantly increased Nestin + and α-internexin + cortical cells. Multiplex ELISA and RT-PCR showed decreased pro-inflammatory cytokine production by microglia co-cultured with injured cortical cells. Inhibition of AKT phosphorylation in cortical cells blocked microglial-enhanced cortical cell viability and expression of neurogenic markers in vitro. This in vitro model system allows for assessment of the effect of microglial-derived soluble signals on cortical cell viability, proliferation, and stages of differentiation during homeostasis or following mechanical injury. These data suggest that microglia cells can downregulate inflammatory cytokine production following activation by mechanical injury to enhance proliferation of new cells capable of neurogenesis via activation of AKT intracellular signaling. Increasing our understanding of the mechanisms that drive microglial-enhanced cortical neurogenesis during homeostasis and following injury in vitro will provide useful information for future primary cell and in vivo studies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.002 |
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".