Comparing Effects of Transforming Growth Factor β1 on Microglia From Rat and Mouse: Transcriptional Profiles and Potassium Channels
Bibliographic record
Abstract
The cytokine, transforming growth factor 1 (TGF1), is up-regulated after CNS injuries or diseases involving microglial activation, and it has been proposed as a therapeutic agent for treating neuroinflammation. Microglia can produce and respond to TGF1. While rats and mice are commonly used for studying neuroinflammation, very few reports directly compare them. Such studies are important for improving pre-clinical studies and furthering translational progress in developing therapeutic interventions. After intracerebral hemorrhage in the rat striatum, the TGF1 receptor was highly expressed on microglia/macrophages within the hematoma. We recently found species similarities and differences in response to either a pro-inflammatory (IFN-γ+TNF-α) or anti-inflammatory (IL-4) stimulus. Here, we assessed whether rat and mouse microglia differ in their responses to TGFβ1. Microglia were isolated from Sprague-Dawley rats and C57BL/6 mice and treated with TGFβ1. We quantified changes in expression of >50 genes, in their morphology, proliferation, apoptosis, and in three potassium channels that are considered therapeutic targets. Many inflammatory mediators, immune receptors and modulators showed species similarities, but notable differences included that, for some genes, only one species responded (e.g., Il4r, Il10, Tgfbr2, Csf1r, Itgam, Socs1, Tlr4, P2rx7, P2ry12), and opposite responses were seen for others (Tgfb1, Myc, Ifngr1). In rat only, TGFβ1 affected microglial morphology and proliferation, but there was no apoptosis in either species. In both species, TGFβ1 dramatically increased Kv1.3 channel expression and current (no effects on Kir2.1). KCa3.1 showed opposite species responses: the current was low in unstimulated rat microglia and greatly increased by TGFβ1 but higher in control mouse cells and decreased by TGF1. Finally, we compared TGF1 and IL10 (often considered similar anti-inflammatory stimuli) and found many different responses in both species. Overall, the numerous species differences should be considered when characterizing neuroinflammation and microglial activation in vitro and in vivo, and when targeting potassium channels.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".