Disease-associated microglia are implicated in neuropsychiatric manifestations of systemic lupus erythematosus
Bibliographic record
Abstract
Abstract Systemic lupus erythematosus (SLE) is an autoimmune disease affecting multiple organs, including the brain. Though 50% of patients may experience neuropsychiatric symptoms (NPSLE), disease mechanisms remain largely unknown. Microglia, the resident innate immune cell of the brain, have recently been implicated in NPSLE. Microglia are heterogenous and may be associated with a homeostatic microglia (CD11clo) or a disease-associated microglia (DAM; CD11chi) state common to neurodegenerative disease and aging. Yet, few studies have examined microglia subsets in NPSLE. We showed that expression of a shared NPSLE transcriptional signature and DAM-associated genes correlates with the severity of behavioral deficits in microglia isolated from two NPSLE models prior to overt systemic disease. Further, our single-cell RNA-seq data identify homeostatic microglia and DAM states in control and NPSLE-prone mice. DAM in NPSLE are enriched for genes associated with antigen presentation but depleted for genes associated with phagocytosis, which is in contrast to DAM in neurodegenerative disease that are critical for phagocytic functions. Moreover, NPSLE CD11clo microglia upregulate genes linked to synapse pruning and phagocytosis, consistent with exacerbated synaptic pruning by microglia in models of NPSLE. We also find that restricted expression of the DAM transcriptional program in NPSLE DAM corresponds to improved behavioral outcomes in NPSLE-prone mice following treatment with fingolimod, a sphingosine-1-phosphate receptor modulator that reduces microglia activation and improves blood brain barrier integrity. These discoveries mark the first to implicate DAM as a potentially pathogenic microglia subset in NPSLE.
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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 | 0.001 | 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) | 0.002 | 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 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".