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A Network-Based Bioinformatics Approach to Identify Molecular Biomarkers for Type 2 Diabetes that Are Linked to the Progression of Neurological Diseases

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  • Md Habibur Rahman

    (Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
    University of Chinese Academy of Sciences, Beijing 100190, China
    Department of Computer Science and Engineering, Islamic University, Kushtia 7003, Bangladesh)

  • Silong Peng

    (Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
    University of Chinese Academy of Sciences, Beijing 100190, China)

  • Xiyuan Hu

    (Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
    University of Chinese Academy of Sciences, Beijing 100190, China)

  • Chen Chen

    (Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
    University of Chinese Academy of Sciences, Beijing 100190, China)

  • Md Rezanur Rahman

    (Department of Biochemistry and Biotechnology, Khwaja Yunus Ali University, Enayetpur, Sirajgonj 6751, Bangladesh)

  • Shahadat Uddin

    (Complex Systems Research Group & Project Management Program, Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia)

  • Julian M.W. Quinn

    (Bone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia)

  • Mohammad Ali Moni

    (Bone Biology Division, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia
    School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia)

Abstract

Neurological diseases (NDs) are progressive disorders, the progression of which can be significantly affected by a range of common diseases that present as comorbidities. Clinical studies, including epidemiological and neuropathological analyses, indicate that patients with type 2 diabetes (T2D) have worse progression of NDs, suggesting pathogenic links between NDs and T2D. However, finding causal or predisposing factors that link T2D and NDs remains challenging. To address these problems, we developed a high-throughput network-based quantitative pipeline using agnostic approaches to identify genes expressed abnormally in both T2D and NDs, to identify some of the shared molecular pathways that may underpin T2D and ND interaction. We employed gene expression transcriptomic datasets from control and disease-affected individuals and identified differentially expressed genes (DEGs) in tissues of patients with T2D and ND when compared to unaffected control individuals. One hundred and ninety seven DEGs (99 up-regulated and 98 down-regulated in affected individuals) that were common to both the T2D and the ND datasets were identified. Functional annotation of these identified DEGs revealed the involvement of significant cell signaling associated molecular pathways. The overlapping DEGs (i.e., seen in both T2D and ND datasets) were then used to extract the most significant GO terms. We performed validation of these results with gold benchmark databases and literature searching, which identified which genes and pathways had been previously linked to NDs or T2D and which are novel. Hub proteins in the pathways were identified (including DNM2, DNM1, MYH14, PACSIN2, TFRC, PDE4D, ENTPD1, PLK4, CDC20B, and CDC14A) using protein-protein interaction analysis which have not previously been described as playing a role in these diseases. To reveal the transcriptional and post-transcriptional regulators of the DEGs we used transcription factor (TF) interactions analysis and DEG-microRNAs (miRNAs) interaction analysis, respectively. We thus identified the following TFs as important in driving expression of our T2D/ND common genes: FOXC1, GATA2, FOXL1, YY1, E2F1, NFIC, NFYA, USF2, HINFP, MEF2A, SRF, NFKB1, USF2, HINFP, MEF2A, SRF, NFKB1, PDE4D, CREB1, SP1, HOXA5, SREBF1, TFAP2A, STAT3, POU2F2, TP53, PPARG, and JUN. MicroRNAs that affect expression of these genes include mir-335-5p, mir-16-5p, mir-93-5p, mir-17-5p, mir-124-3p. Thus, our transcriptomic data analysis identifies novel potential links between NDs and T2D pathologies that may underlie comorbidity interactions, links that may include potential targets for therapeutic intervention. In sum, our neighborhood-based benchmarking and multilayer network topology methods identified novel putative biomarkers that indicate how type 2 diabetes (T2D) and these neurological diseases interact and pathways that, in the future, may be targeted for treatment.

Suggested Citation

  • Md Habibur Rahman & Silong Peng & Xiyuan Hu & Chen Chen & Md Rezanur Rahman & Shahadat Uddin & Julian M.W. Quinn & Mohammad Ali Moni, 2020. "A Network-Based Bioinformatics Approach to Identify Molecular Biomarkers for Type 2 Diabetes that Are Linked to the Progression of Neurological Diseases," IJERPH, MDPI, vol. 17(3), pages 1-25, February.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:3:p:1035-:d:317366
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    References listed on IDEAS

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    1. Taylor W. Schmitz & R. Nathan Spreng, 2016. "Basal forebrain degeneration precedes and predicts the cortical spread of Alzheimer’s pathology," Nature Communications, Nature, vol. 7(1), pages 1-13, December.
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