Immunogenic self-DNA drives neuropsychiatric systemic lupus erythematosus in conventional genetic background mice
Bibliographic record
Abstract
Neuropsychiatric systemic lupus erythematosus (NPSLE) represents a significant and growing challenge in both clinical practice and research, with its mechanistic investigation hindered by the lack of reliable animal models. Over the past two decades, we have established that immunogenic self-DNA can induce SLE disease model, which has been widely utilized in the academic community. To modify the doses of immunogenic self-DNA, validate the induction of SLE disease, and systematically characterize the resulting neuropsychiatric manifestations, aiming to provide an optimal model for NPSLE. Conventional genetic background BALB/c mice were immunized with 75 µg of immunogenic self-DNA. Based on the criteria and diagnostic recommendations from the ACR and EULAR, we conducted neurobehavioral experiments to assess the neuropsychiatric manifestations of clinical NPSLE patients. Whole-cell patch-clamp electrophysiological recordings were performed on mouse brain slices to assess electroencephalographic (EEG) abnormalities associated with NPSLE. Cerebrospinal fluid (CSF) abnormalities were evaluated by measuring inflammatory factors in the CSF. Additionally, histopathological analyses were conducted to evaluate MRI abnormalities in self-DNA immunized mice. Self-DNA immunized mice developed progressive cognitive impairments, exhibiting spatial and working memory deficits from week 8 post-immunization, which worsened by week 12, alongside the emergence of anxiety-like and depression-like behaviors. In parallel, electrophysiological analysis revealed synaptic transmission deficits and reduced neuronal excitability beginning at week 8, further deteriorating by week 12. Of note, blood-brain barrier (BBB) disruption was observed at 4-8 weeks post immunization, which was evidenced by IgG leakage and FITC-dextran extravasation. Such BBB disruption was accompanied by elevated pro-inflammatory cytokines (IL-1β, IL-6, TNF-α, IL-10), resembling neuropsychiatric lupus pathology. Finally, histologically, hippocampal neuronal loss and dendritic spine reduction in CA1, CA3, and DG subregions were observed, providing structural correlates for the observed memory deficits in self-DNA immunized mice. This model induced by immunogenic self-DNA recapitulated the neurological manifestations observed in clinical patients, rendering it a robust model for the research of 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".