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Record W2801317885 · doi:10.3389/fncel.2018.00115

Comparing Effects of Transforming Growth Factor β1 on Microglia From Rat and Mouse: Transcriptional Profiles and Potassium Channels

2018· article· en· W2801317885 on OpenAlexafffund
Starlee Lively, Doris Lam, Raymond Wong, Lyanne C. Schlichter

Bibliographic record

VenueFrontiers in Cellular Neuroscience · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health ResearchCanadian Stroke Network
KeywordsMicrogliaNeuroinflammationTransforming growth factorCytokineBiologyReceptorTumor necrosis factor alphaTLR4Cell biologyNeuroscienceInflammationImmunologyCancer researchBiochemistry

Abstract

fetched live from OpenAlex

The cytokine, transforming growth factor 1 (TGF1), 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 TGF1. 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 TGF1 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 TGF1. Finally, we compared TGF1 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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.223
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2018
Admission routes2
Has abstractyes

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