Age-Dependent Synthesis of Diverse Autoantibodies in the 3xTg-AD Model of Alzheimer’s Disease
Bibliographic record
Abstract
Abstract Elevated levels of serum autoantibodies are a well-documented phenomenon in patients with Alzheimer’s disease (AD), but the nature of the autoimmunity is unclear. Triple-transgenic (3xTg-AD) mice are a well-established model that develops AD-like pathology. We observed that recent cohorts of male 3xTg-AD mice exhibit autoimmune manifestations, yet lack plaque/tangle pathology. In the current study, we identified the time course and type of autoantibodies produced during the progression and attenuation of systemic autoimmunity. Tissue samples were collected from 3xTg-AD and wild type (WT) males and females at 2, 6 and 12 months of age. Serum autoantibodies were measured by both indirect immunofluorescence and 16-antigen line immunoassay. To assess immunosuppression, serum samples from a separate cohort of mice chronically exposed to cyclophosphamide were also assessed. Several autoantibody types increased in frequency and concentration in aging 3xTg-AD mice, particularly in adult males. The most prevalent were anti-nuclear autoantibodies, particularly those targeting the nucleosomes. Among the panel of autoantibodies measured, reactivity to a single antigen (nucleosomes) was observed in the majority of samples. A few mice showed reactivity to other autoantigens, such as histones, dsDNA, RNP/Sm, AMA-M2 and rib. P. Reactivity to all autoantigens was abolished in mice exposed to the immunosuppressant cyclophosphamide. The emergence of circulating autoantibodies similar to classical autoimmune diseases (such as lupus) suggests an important, yet unreported aspect of pathology in this AD model. Future studies are aimed to elucidate the origin and role of the autoantibodies in the context of phenotype loss in 3xTg-AD males.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".