Identification of molecular signatures and pathways to identify novel therapeutic targets in Alzheimer's disease: Insights from a systems biomedicine perspective.

Gov E

Rahman MR(1), Islam T(2), Zaman T(3), Shahjaman M(4), Karim MR(3), Huq F(5), Quinn JMW(6), Holsinger RMD(7), Gov E(8), Moni MA(9).
Author information:
(1)Department of Biochemistry and Biotechnology, School of Biomedical Science, Khwaja Yunus Ali University, Enayetpur, Sirajgonj, Bangladesh. Electronic address: [Email]
(2)Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh.
(3)Department of Biochemistry and Biotechnology, School of Biomedical Science, Khwaja Yunus Ali University, Enayetpur, Sirajgonj, Bangladesh.
(4)Department of Statistics, Begum Rokeya University, Rangpur, Bangladesh.
(5)Discipline of Pathology, School of Medical Sciences, The University of Sydney, NSW 2006, Australia.
(6)Bone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.
(7)Discipline of Pathology, School of Medical Sciences, The University of Sydney, NSW 2006, Australia; Laboratory of Molecular Neuroscience and Dementia, Brain and Mind Centre, The University of Sydney, Camperdown, NSW 2006, Australia.
(8)Department of Bioengineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey.
(9)Discipline of Pathology, School of Medical Sciences, The University of Sydney, NSW 2006, Australia; Bone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia. Electronic address: [Email]

https://dx.doi.org/10.1016/j.ygeno.2019.07.018

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. However, there are no peripheral biomarkers available that can detect AD onset. This study aimed to identify the molecular signatures in AD through an integrative analysis of blood gene expression data. We used two microarray datasets (GSE4226 and GSE4229) comparing peripheral blood transcriptomes of AD patients and controls to identify differentially expressed genes (DEGs). Gene set and protein overrepresentation analysis, protein-protein interaction (PPI), DEGs-Transcription Factors (TFs) interactions, DEGs-microRNAs (miRNAs) interactions, protein-drug interactions, and protein subcellular localizations analyses were performed on DEGs common to the datasets. We identified 25 common DEGs between the two datasets. Integration of genome scale transcriptome datasets with biomolecular networks revealed hub genes (NOL6, ATF3, TUBB, UQCRC1, CASP2, SND1, VCAM1, BTF3, VPS37B), common transcription factors (FOXC1, GATA2, NFIC, PPARG, USF2, YY1) and miRNAs (mir-20a-5p, mir-93-5p, mir-16-5p, let-7b-5p, mir-708-5p, mir-24-3p, mir-26b-5p, mir-17-5p, mir-193-3p, mir-186-5p). Evaluation of histone modifications revealed that hub genes possess several histone modification sites associated with AD. Protein-drug interactions revealed 10 compounds that affect the identified AD candidate biomolecules, including anti-neoplastic agents (Vinorelbine, Vincristine, Vinblastine, Epothilone D, Epothilone B, CYT997, and ZEN-012), a dermatological (Podofilox) and an immunosuppressive agent (Colchicine). The subcellular localization of molecular signatures varied, including nuclear, plasma membrane and cytosolic proteins. In the present study, it was identified blood-cell derived molecular signatures that might be useful as candidate peripheral biomarkers in AD. It was also identified potential drugs and epigenetic data associated with these molecules that may be useful in designing therapeutic approaches to ameliorate AD.