Transcriptomic Profiling of Primary Microglia: Effects of miR-19a-3p and miR-19b-3p on Microglia Activation
Bibliographic record
Abstract
Neuropathic pain resulting from spinal cord injury (SCI) is a significant secondary health issue affecting around 60% of individuals with SCI. After SCI, activation of microglia, the immune cells within the central nervous system, leads to neuroinflammation by producing pro-inflammatory cytokines and affects neuropathic pain. This interplay between inflammation and pain contributes to the persistent and intense pain experienced by many individuals with SCI. MicroRNAs (miRs) have been critical regulators of neuroinflammation. Previous research in our laboratory has revealed upregulation levels of circulating miR-19a and miR-19b in individuals with SCI with neuropathic pain compared to those without pain. In this study, we treated primary microglial cultures from mice with miR-19a and miR-19b for 24 h and conducted RNA sequencing analysis. Our results showed that miR-19a and miR-19b up- and downregulate different genes according to the volcano plots and the heatmaps. miR-19a and miR-19b regulate inflammation through distinct signaling pathways. The results showed that miR-19a promotes inflammation via toll-like receptor signaling, TNF signaling, and cytokine-cytokine receptor interactions, while miR-19b increases inflammatory responses through the PI3K-Akt signaling pathway, focal adhesion, and extracellular matrix receptor interactions. The protein-protein interaction (PPI) networks used the STRING database to identify transcription factors associated with genes up- or downregulated by miR-19a and miR-19b. Key transcription factors, such as STAT1, STAT2, and KLF4 for miR-19a, and Nr4a1, Nr4a2, and Nr4a3 for miR-19b, were identified and revealed their roles in regulating neuroinflammation. This study demonstrates that miR-19a and miR-19b modulate diverse patterns of gene expression, regulate inflammation, and induce inflammatory responses in microglia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".