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Proposed Minimal Essential Co-Expression and Physical Interaction Networks Involved in the Development of Cognition Impairment in Human Mid and Late Life Publisher Pubmed



Salehi Z1 ; Arabfard M2 ; Sadatpour O1 ; Ohadi M3
Authors
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Authors Affiliations
  1. 1. Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
  3. 3. Iranian Research Center on Aging, University of Social Welfare and Rehabilitation Sciences, Kudakyar Alley, Daneshju Blvd., Evin, Tehran, Iran

Source: Neurological Sciences Published:2021


Abstract

Aim: The aim of this study was to identify the minimal essential co-expression and physical interaction networks involved in the development of cognition impairment in human mid and late life. Methods: We searched the Online Mendelian Inheritance in Man (OMIM) database to extract the validated human genes annotated (until March 2020) for five major disorders of pathophysiological overlap and sequential chronological occurrence in human, including multiple sclerosis, type 2 diabetes mellitus, Alzheimer’s disease, vascular dementia, and Lewy body dementia. Gene co-expression and physical interaction networks were subsequently constructed for the overlapping genes across the selected disorders. Results: Remarkably, each of the gene co-expression and physical interaction networks consisted of single clusters (P = 0.0005 and P = 1 × 10−16, respectively). APP was the major hub in the integrated and tissue-specific co-expression networks, whereas insulin was the major hub in the physical interaction network. Several other hubs were identified across the identified networks, including TNF, VEGFA, GAPDH, and NOTCH1. Conclusion: We propose the minimal co-expression and physical interaction networks and their single clustering in the development of cognition impairment in human mid and late life. This is a pilot study, warranting identification of more risk genes, using additional validated databases in the future. © 2020, Fondazione Societa Italiana di Neurologia.