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DOI 10.1007/s10072-026-09364-y

Медицинская библиотека · Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026

Cross-disease protein aggregate interactions in neurodegeneration: from molecular mechanisms to therapeutic strategies.

Yang Wenqi, Lu Yan

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Аннотация

BACKGROUND

Neurodegenerative diseases have traditionally been classified by their predominant protein pathology. However, accumulating evidence reveals extensive molecular cross-talk between distinct pathological proteins, particularly in Alzheimer's disease (AD).

OBJECTIVE

This review examines cross-disease protein interactions and their implications for diagnosis and treatment.

METHODS

We systematically reviewed peer-reviewed literature published between 2020 and 2025, complemented by seminal earlier studies, examining molecular mechanisms of protein cross-seeding, clinical evidence of co-pathology, and emerging therapeutic strategies.

RESULTS

Cross-seeding between amyloid-β (Aβ), tau, α-synuclein, and TDP-43 has been demonstrated in vitro and in vivo. Harmonized autopsy studies reveal that 91% of individuals over 80 years harbor multiple neuropathologies. Co-pathology accelerates cognitive decline and complicates biomarker interpretation. Anti-amyloid immunotherapies lecanemab and donanemab demonstrate 27-35% slowing of cognitive decline, while anti-tau antibodies targeting N-terminal epitopes have uniformly failed, prompting investigation of combination approaches.

CONCLUSIONS

Recognition of cross-disease protein interactions necessitates a paradigm shift toward multi-target therapeutic strategies and precision medicine approaches accounting for individual co-pathology burden.

Для цитирования: Yang Wenqi, Lu Yan. Cross-disease protein aggregate interactions in neurodegeneration: from molecular mechanisms to therapeutic strategies.. Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology. 2026. DOI: 10.1007/s10072-026-09364-y.

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