A Network-Based Bioinformatics Approach to Identify Molecular Biomarkers for Type 2 Diabetes that Are Linked to the Progression of Neurological Diseases
Rahman, Md Habibur1,2,3; Peng, Silong1,2; Hu, Xiyuan1,2; Chen, Chen1,2; Rahman, Md Rezanur4; Uddin, Shahadat5,6; Quinn, Julian M. W.7; Moni, Mohammad Ali7,8
发表期刊INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
ISSN1661-7827
2020-02-01
卷号17期号:3页码:25
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.

关键词bioinformatics computational biology gene ontology protein pathways type 2 diabetes neurological disease
DOI10.3390/ijerph17031035
关键词[WOS]HUNTINGTONS-DISEASE ; MULTIPLE-SCLEROSIS ; ALZHEIMERS-DISEASE ; METAANALYSIS ; DATABASE ; PATHWAY ; RISK ; ALS ; PATHOGENESIS ; ASSOCIATION
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[61571438] ; National Natural Science Foundation of China[61571438]
WOS研究方向Environmental Sciences & Ecology ; Public, Environmental & Occupational Health
WOS类目Environmental Sciences ; Public, Environmental & Occupational Health
WOS记录号WOS:000517783300360
出版者MDPI
七大方向——子方向分类人工智能+医疗
引用统计
被引频次:39[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/38452
专题智能制造技术与系统研究中心_多维数据分析
通讯作者Moni, Mohammad Ali
作者单位1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
3.Islamic Univ, Dept Comp Sci & Engn, Kushtia 7003, Bangladesh
4.Khwaja Yunus Ali Univ, Dept Biochem & Biotechnol, Enayetpur 6751, Sirajgonj, Bangladesh
5.Univ Sydney, Complex Syst Res Grp, Fac Engn, Sydney, NSW 2006, Australia
6.Univ Sydney, Project Management Program, Fac Engn, Sydney, NSW 2006, Australia
7.Garvan Inst Med Res, Bone Biol Div, Darlinghurst, NSW 2010, Australia
8.Univ Sydney, Sch Med Sci, Fac Med & Hlth, Sydney, NSW 2006, Australia
第一作者单位中国科学院自动化研究所
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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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