miR‐155 as a multiple sclerosis–relevant regulator of myeloid cell polarization
Bibliographic record
Abstract
OBJECTIVE: To define the functional significance of increased miR-155 expression in myeloid cells in multiple sclerosis (MS). METHODS: miR-155 expression levels were measured in CD14+ monocytes from untreated relapsing-remitting MS patients and compared to healthy controls. Similar microRNA (miRNA) analyses were performed in laser-captured CD68+ cells from perivascular (blood-derived macrophages) and parenchymal (microglia) brain regions in both active MS lesions and noninflammatory cases. Using human adult blood-derived macrophages and brain-derived microglia, in vitro experiments were performed to demonstrate how miR-155 influences the polarization state, phenotype, and functional properties of myeloid cells, in addition to their ability to subsequently impact adaptive T-cell responses. RESULTS: In MS, miR-155 expression was significantly increased in both peripheral circulating CD14+ monocytes and active lesions (CD68+ cells) compared to control donor monocytes and parenchymal microglia, respectively. In vitro, miR-155 was significantly increased in both M1-polarized primary human macrophages and microglia. Transfection of an miR-155 mimic increased proinflammatory cytokine secretion and costimulatory surface marker expression in both cell types; an miR-155 inhibitor decreased proinflammatory cytokine expression. Coculture experiments demonstrated that allogeneic T-cell responses were significantly enhanced in the presence of miR-155-transfected myeloid cells compared to controls. INTERPRETATION: Our results demonstrate that miR-155 regulates proinflammatory responses in both blood-derived and central nervous system (CNS)-resident myeloid cells, in addition to impacting subsequent adaptive immune responses. Differential miRNA expression may therefore provide insight into mechanisms responsible for distinct phenotypic and functional properties of myeloid cells, thus impacting their ability to influence CNS injury and repair.
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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 | 0.001 |
| 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) | 0.000 | 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".