TAAR1 Expression in Human Macrophages and Brain Tissue: A Potential Novel Facet of MS Neuroinflammation
Bibliographic record
Abstract
TAAR1 is a neuroregulator with emerging evidence suggesting a role in immunomodulation. Multiple sclerosis (MS) is an immune-mediated demyelinating disease of the central nervous system. Here, we investigate TAAR1 expression in human primary monocytes, peripherally-derived macrophages, and MS brain tissue. RT-qPCR was used to assess TAAR1 levels in MS monocytes. Using a previously validated anti-human TAAR1 antibody and fluorescence microscopy, TAAR1 protein was visualized in lipopolysaccharide-stimulated or basal human macrophages, as well as macrophage/microglia populations surrounding, bordering, and within a mixed active/inactive MS lesion. In vivo, TAAR1 mRNA expression was significantly lower in MS monocytes compared to age- and sex-matched healthy controls. In vitro, TAAR1 protein showed a predominant nuclear localization in quiescent/control macrophages with a shift to a diffuse intracellular distribution following lipopolysaccharide-induced activation. In brain tissue, TAAR1 protein was predominantly expressed in macrophages/microglia within the border region of mixed active/inactive MS lesions. Considering that TAAR1-mediated anti-inflammatory effects have been previously reported, decreased mRNA in MS patients suggests possible pathophysiologic relevance. A shift in TAAR1 localization following pro-inflammatory activation suggests its function is altered in pro-inflammatory states, while TAAR1-expressing macrophages/microglia bordering an MS lesion supports TAAR1 as a novel pharmacological target in cells directly implicated in MS neuroinflammation.
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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) | 0.001 | 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".