AI guided discovery of a murine model of asymptomatic Alzheimer's disease.

Overview

1. Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, United States
2. Department of Pediatrics, University of California San Diego, 9500 Gilman Drive, MC 0703 Israni Biomedical Research Facility Room No 2119, La Jolla, CA 92093 USA
3. Department of Computer Science and Engineering, University of California San Diego, La Jolla, United States
4. Veterans Medical Research Foundation, 3350 La Jolla Village Drive (151A), San Diego, CA 92161 USA
5. Metabolic Physiology & Ultrastructural Biology Laboratory, Department of Medicine, University of California, San Diego (0732), 9575 Gilman Drive; Stein Clinical Research Building #207, La Jolla, CA 92093-0703 USA
6. Feil Family Brain and Mind Research Institute, Helen and Robert Appel Alzheimer’s Disease Research Institute, Weill Cornell Medicine, 413 East 69th Street, New York, NY 10021 USA
7. VA San Diego Healthcare System, 3350 La Jolla Village Drive, San Diego, CA 92161 USA
8. Department of Anesthsiology, University of California San Diego, La Jolla, United States

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder defined by extracellular deposition of amyloid-β (Aβ) plaques and intracellular accumulation of hyperphosphorylated Tau in neurofibrillary tangles (NFTs). Notably, approximately 20–30% of older individuals harbor substantial amyloid and Tau pathology yet remain cognitively intact, a clinically silent state referred to as asymptomatic Alzheimer’s disease (AsymAD). The biological basis of this cognitive resilience remains poorly understood, in large part due to the absence of mechanistic frameworks and preclinical models that dissociate neuropathology from cognitive decline. Here, we integrate systems-level Boolean network modeling with in vivo validation to define the transcriptomic logic of AsymAD and establish an experimentally tractable murine model of cognitive resilience. Boolean implication networks trained on large-scale human cortical RNA-sequencing datasets identified a robust, invariant AD gene signature that accurately stratified disease states across multiple independent cohorts. Reverse translation of this signature to transgenic mouse models revealed a striking dissociation between molecular pathology and behavioral outcome in Chromogranin A (CgA)–deficient PS19 mice (CgA-KO/PS19). Male CgA-KO/PS19 mice exhibited AD-like transcriptomic and neuropathological features in the prefrontal cortex while retaining intact learning and memory. Female CgA-KO/PS19 mice demonstrated even greater resilience, characterized by suppression of Tau aggregation and preservation of synaptic ultrastructure. Together, these findings establish a validated murine model of AsymAD and identify CgA as a modifiable molecular node linking neuroendocrine signaling, Tauopathy, and cognitive preservation. This integrative computational–experimental framework provides a scalable and generalizable platform for dissecting sex-specific mechanisms of cognitive resilience, identifying early biomarkers of disease trajectory, and enabling mechanism-guided development of preventive therapeutic strategies for AD.

Supplementary Information: The online version contains supplementary material available at 10.1186/s40478-026-02286-y.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• figshare:32353660, at figshare; found in DataCite
• geo:GSE125583, at NCBI GEO; found in the text, “Boolean network modeling identifies an…”

Data availability

All data are available in the main text or the supplementary materials. The codes are available at [https://github.com/sahoo00/ADnet](https:/github.com/sahoo00/ADnet). We created Boolean Lab Alzheimer’s Disease Benchmark (BoLAD benchmark) based on the training and validation datasets which can be downloaded from the link in the github page.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 7 keywords, 13 MeSH terms, 9 funders, 101 references.

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This paper

Jati, S., Taheri, S., Kal, S., Sinha, S. C., Head, B. P., Mahata, S. K., & Sahoo, D. (2026). AI guided discovery of a murine model of asymptomatic Alzheimer's disease. Acta neuropathologica communications, 14(1), 110. https://doi.org/10.1186/s40478-026-02286-y

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