A Network-Based Bioinformatics Approach to Identify Molecular Biomarkers for Type 2 Diabetes that Are Linked to the Progression of Neurological Diseases
文献类型：期刊论文
| 作者 | Rahman, Md Habibur3,7,8 ; Peng, Silong3,7; Hu, Xiyuan3,7; Chen, Chen3,7; Rahman, Md Rezanur4; Uddin, Shahadat1,2; Quinn, Julian M. W.6; Moni, Mohammad Ali5,6
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| 刊名 | INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
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| 出版日期 | 2020-02-01 |
| 卷号 | 17期号:3页码:25 |
| ISSN号 | 1661-7827 |
| 关键词 | bioinformatics computational biology gene ontology protein pathways type 2 diabetes neurological disease |
| DOI | 10.3390/ijerph17031035 |
| 通讯作者 | Moni, Mohammad Ali(mohammad.moni@sydney.edu.au) |
| 英文摘要 | 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. |
| WOS关键词 | HUNTINGTONS-DISEASE ; MULTIPLE-SCLEROSIS ; ALZHEIMERS-DISEASE ; METAANALYSIS ; DATABASE ; PATHWAY ; RISK ; ALS ; PATHOGENESIS ; ASSOCIATION |
| 资助项目 | National Natural Science Foundation of China[61571438] |
| WOS研究方向 | Environmental Sciences & Ecology ; Public, Environmental & Occupational Health |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000517783300360 |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/38451]  |
| 专题 | 自动化研究所_智能制造技术与系统研究中心_多维数据分析团队 |
| 通讯作者 | Moni, Mohammad Ali |
| 作者单位 | 1.Univ Sydney, Project Management Program, Fac Engn, Sydney, NSW 2006, Australia 2.Univ Sydney, Complex Syst Res Grp, Fac Engn, Sydney, NSW 2006, Australia 3.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 4.Khwaja Yunus Ali Univ, Dept Biochem & Biotechnol, Enayetpur 6751, Sirajgonj, Bangladesh 5.Univ Sydney, Sch Med Sci, Fac Med & Hlth, Sydney, NSW 2006, Australia 6.Garvan Inst Med Res, Bone Biol Div, Darlinghurst, NSW 2010, Australia 7.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 8.Islamic Univ, Dept Comp Sci & Engn, Kushtia 7003, Bangladesh |
| 推荐引用方式 GB/T 7714 | Rahman, Md Habibur,Peng, Silong,Hu, Xiyuan,et al. A Network-Based Bioinformatics Approach to Identify Molecular Biomarkers for Type 2 Diabetes that Are Linked to the Progression of Neurological Diseases[J]. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH,2020,17(3):25. |
| APA | Rahman, Md Habibur.,Peng, Silong.,Hu, Xiyuan.,Chen, Chen.,Rahman, Md Rezanur.,...&Moni, Mohammad Ali.(2020).A Network-Based Bioinformatics Approach to Identify Molecular Biomarkers for Type 2 Diabetes that Are Linked to the Progression of Neurological Diseases.INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH,17(3),25. |
| MLA | Rahman, Md Habibur,et al."A Network-Based Bioinformatics Approach to Identify Molecular Biomarkers for Type 2 Diabetes that Are Linked to the Progression of Neurological Diseases".INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 17.3(2020):25. |
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来源：自动化研究所
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