Cross-disease analysis of Alzheimer’s disease and type-2 Diabetes highlights the role of autophagy in the pathophysiology of two highly comorbid ... View Full Text


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Article Info

DATE

2019-12

AUTHORS

Laura Caberlotto, T.-Phuong Nguyen, Mario Lauria, Corrado Priami, Roberto Rimondini, Silvia Maioli, Angel Cedazo-Minguez, Giulia Sita, Fabiana Morroni, Mauro Corsi, Lucia Carboni

ABSTRACT

Evidence is accumulating that the main chronic diseases of aging Alzheimer's disease (AD) and type-2 diabetes mellitus (T2DM) share common pathophysiological mechanisms. This study aimed at applying systems biology approaches to increase the knowledge of the shared molecular pathways underpinnings of AD and T2DM. We analysed transcriptomic data of post-mortem AD and T2DM human brains to obtain disease signatures of AD and T2DM and combined them with protein-protein interaction information to construct two disease-specific networks. The overlapping AD/T2DM network proteins were then used to extract the most representative Gene Ontology biological process terms. The expression of genes identified as relevant was studied in two AD models, 3xTg-AD and ApoE3/ApoE4 targeted replacement mice. The present transcriptomic data analysis revealed a principal role for autophagy in the molecular basis of both AD and T2DM. Our experimental validation in mouse AD models confirmed the role of autophagy-related genes. Among modulated genes, Cyclin-Dependent Kinase Inhibitor 1B, Autophagy Related 16-Like 2, and insulin were highlighted. In conclusion, the present investigation revealed autophagy as the central dys-regulated pathway in highly co-morbid diseases such as AD and T2DM allowing the identification of specific genes potentially involved in disease pathophysiology which could become novel targets for therapeutic intervention. More... »

PAGES

3965

References to SciGraph publications

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-019-39828-5

    DOI

    http://dx.doi.org/10.1038/s41598-019-39828-5

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112641490

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30850634


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