Implicating MicroRNAs as Regulators of Microglia and Astrocyte Responses in Human CNS Inflammatory Disease (P5.018)
Bibliographic record
Abstract
OBJECTIVE: To characterize and compare cell-specific microRNA (miRNA) expression in human astrocytes and microglia/macrophages within the inflamed human CNS and demonstrate their capacity to regulate disease-relevant responses. BACKGROUND: The functional role of astrocytes and microglia in the inflamed CNS remains enigmatic, yet both cell types are important and direct contributors to both inflammatory and repair mechanisms following CNS injury. MiRNAs are non-coding RNA molecules involved in regulating post-translational activities of mRNA transcripts. We hypothesize that expression of distinct miRNAs in glial cells will influence the molecular mechanisms responsible for their unique properties under inflammatory conditions. Identifying differential patterns of miRNA expression may provide insight into the capacity for glial cells to influence CNS injury and repair. DESIGN/METHODS: Laser-capture microdissection (LCM) was used to isolate CD68+ and GFAP+ cells from formalin-fixed autopsy control and MS brain samples. MiRNA expression was determined by TaqMan qPCR miRNA expression assays. For in vitro studies, astrocytes and microglia were isolated from fetal/adult CNS tissue and activated using IL-1ß and LPS, respectively. Transfection of miRNA mimics and inhibitors was performed using a RNAi lipofectamine reagent. RESULTS: In situ LCM miRNA analysis of microglia/ macrophages and astrocytes in control brain versus MS lesions demonstrated differential expression of a subset of inflammation-related miRNAs. Guided by these results, transfection of either a mir-146a inhibitor or a mir-125b mimic in vitro, using isolated human microglia and macrophages, increased LPS-induced expression of TNF and IL-6. In astrocytes, mimics of both mir-365 and mir-146b-5p significantly attenuated IL-1ß-induced IL-6 release. Astrocyte CXCL10 (IP-10) expression was also decreased following transfection with a mir-146b-5p mimic, but was not influenced by mir-365 mimic. CONCLUSIONS: Our results demonstrate that unique miRNAs, implicated in situ within astrocytes and microglia/macrophages of MS lesions, can impact glial responses that may be relevant in mechanisms of injury and repair.
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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 | 0.001 |
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".