Differential responses of primary neuron-secreted MCP-1 and IL-9 to type 2 diabetes and Alzheimer’s disease-associated metabolites

Ball, Brendan K.; Kuhn, Madison K.; Fleeman Bechtel, Rebecca M.; Proctor, Elizabeth A.; Brubaker, Douglas K.

doi:10.1038/s41598-024-62155-3

Cited by 4 publications

(1 citation statement)

References 110 publications

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“…These tau oligomers with seeding characteristics have exhibited disruption of proteostatic mechanisms at synapses, oxidative stress through damage to the mitochondria, and synaptic loss in AD patients [ 27 , 28 ]. Recent clinical, in vitro, and in silico studies [ 9 , 29 , 30 , 31 , 32 , 33 , 34 ] have further indicated that tau can interact or cross-seed with amylin, an amyloid protein associated with type 2 diabetes, to form membrane-bound hetero tau–amylin oligomers. These heterooligomers contribute to the molecular crosstalk between AD and type 2 diabetes [ 30 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring Tau Fibril-Disaggregating and Antioxidating Molecules Binding to Membrane-Bound Amyloid Oligomers Using Machine Learning-Enhanced Docking and Molecular Dynamics

Segura,

Santos,

Flores

et al. 2024

Molecules

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Intracellular tau fibrils are sources of neurotoxicity and oxidative stress in Alzheimer’s. Current drug discovery efforts have focused on molecules with tau fibril disaggregation and antioxidation functions. However, recent studies suggest that membrane-bound tau-containing oligomers (mTCOs), smaller and less ordered than tau fibrils, are neurotoxic in the early stage of Alzheimer’s. Whether tau fibril-targeting molecules are effective against mTCOs is unknown. The binding of epigallocatechin-3-gallate (EGCG), CNS-11, and BHT-CNS-11 to in silico mTCOs and experimental tau fibrils was investigated using machine learning-enhanced docking and molecular dynamics simulations. EGCG and CNS-11 have tau fibril disaggregation functions, while the proposed BHT-CNS-11 has potential tau fibril disaggregation and antioxidation functions like EGCG. Our results suggest that the three molecules studied may also bind to mTCOs. The predicted binding probability of EGCG to mTCOs increases with the protein aggregate size. In contrast, the predicted probability of CNS-11 and BHT-CNS-11 binding to the dimeric mTCOs is higher than binding to the tetrameric mTCOs for the homo tau but not for the hetero tau–amylin oligomers. Our results also support the idea that anionic lipids may promote the binding of molecules to mTCOs. We conclude that tau fibril-disaggregating and antioxidating molecules may bind to mTCOs, and that mTCOs may also be useful targets for Alzheimer’s drug design.

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Section: Introductionmentioning

confidence: 99%

Exploring Tau Fibril-Disaggregating and Antioxidating Molecules Binding to Membrane-Bound Amyloid Oligomers Using Machine Learning-Enhanced Docking and Molecular Dynamics

Segura,

Santos,

Flores

et al. 2024

Molecules

View full text Add to dashboard Cite

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Principal components analysis on genes related to inflammasome complex and microglial activation in the hypothalamus of obese mice treated with semaglutide (GLP-1 analog)

Marinho,

Fabiano,

Aguila

et al. 2025

Brain Research

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Cross-disease modeling of peripheral blood identifies biomarkers of type 2 diabetes predictive of Alzheimer’s disease

Ball,

Park,

Proctor

et al. 2024

Preprint

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Type 2 diabetes (T2D) is a significant risk factor for Alzheimer's disease (AD). Despite multiple studies reporting this connection, the mechanism by which T2D exacerbates AD is poorly understood. It is challenging to design studies that address co-occurring and comorbid diseases, limiting the number of existing evidence bases. To address this challenge, we expanded the applications of a computational framework called Translatable Components Regression (TransComp-R), initially designed for cross-species translation modeling, to perform cross-disease modeling to identify biological programs of T2D that may exacerbate AD pathology. Using TransComp-R, we combined peripheral blood-derived T2D and AD human transcriptomic data to identify T2D principal components predictive of AD status. Our model revealed genes enriched for biological pathways associated with inflammation, metabolism, and signaling pathways from T2D principal components predictive of AD. The same T2D PC predictive of AD outcomes unveiled sex-based differences across the AD datasets. We performed a gene expression correlational analysis to identify therapeutic hypotheses tailored to the T2D-AD axis. We identified six T2D and two dementia medications that induced gene expression profiles associated with a non-T2D or non-AD state. Finally, we assessed our blood-based T2DxAD biomarker signature in post-mortem human AD and control brain gene expression data from the hippocampus, entorhinal cortex, superior frontal gyrus, and postcentral gyrus. Using partial least squares discriminant analysis, we identified a subset of genes from our cross-disease blood-based biomarker panel that significantly separated AD and control brain samples. Our methodological advance in cross-disease modeling identified biological programs in T2D that may predict the future onset of AD in this population. This, paired with our therapeutic gene expression correlational analysis, also revealed alogliptin, a T2D medication that may help prevent the onset of AD in T2D patients.

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Differential responses of primary neuron-secreted MCP-1 and IL-9 to type 2 diabetes and Alzheimer’s disease-associated metabolites

Cited by 4 publications

References 110 publications

Exploring Tau Fibril-Disaggregating and Antioxidating Molecules Binding to Membrane-Bound Amyloid Oligomers Using Machine Learning-Enhanced Docking and Molecular Dynamics

Exploring Tau Fibril-Disaggregating and Antioxidating Molecules Binding to Membrane-Bound Amyloid Oligomers Using Machine Learning-Enhanced Docking and Molecular Dynamics

Principal components analysis on genes related to inflammasome complex and microglial activation in the hypothalamus of obese mice treated with semaglutide (GLP-1 analog)

Cross-disease modeling of peripheral blood identifies biomarkers of type 2 diabetes predictive of Alzheimer’s disease

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