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. 2020 Feb 6;17(3):1035.
doi: 10.3390/ijerph17031035.

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

Md Habibur Rahman  1   2   3 , Silong Peng  1   2 , Xiyuan Hu  1   2 , Chen Chen  1   2 , Md Rezanur Rahman  4 , Shahadat Uddin  5 , Julian M W Quinn  6 , Mohammad Ali Moni  6   7
Affiliations

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

Md Habibur Rahman et al. Int J Environ Res Public Health. .

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.

Keywords: bioinformatics; computational biology; gene ontology; neurological disease; pathways; protein; type 2 diabetes.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
An overview of the network-based systematic and quantitative approach. Neurological diseases that were investigated comprised of Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), cerebral palsy (CP), epilepsy disease (ED), Huntington’s disease (HD), multiple sclerosis (MS), and Parkinson’s disease (PD).
Figure 2
Figure 2
Up-regulated Gene-Disease-network (GDN) between type 2 diabetes (T2D) and neurological diseases (NDs) comprising of commonly up-regulated genes node and different categories of diseases node represented by round shaped robin’s egg blue colour and octagon-shaped red colour.
Figure 3
Figure 3
Down-regulated Gene-Disease-network (GDN) between T2D and NDs comprising of commonly down-regulated genes node and different categories of diseases node represented by round shaped robin’s egg blue colour and octagon-shaped red colour.
Figure 4
Figure 4
The protein-protein interactions (PPIs) network is built with the significantly dysregulated genes common to type 2 diabetes (T2D) and neurological diseases (NDs).
Figure 5
Figure 5
The simplified PPIs network is built with the significantly dysregulated genes common to type 2 diabetes (T2D) and neurological diseases (NDs) to identify the 10 most significant hub proteins marked as red, orange and yellow colour.
Figure 6
Figure 6
The DEGs-TF interactions regulating the differentially expressed genes common to type 2 diabetes (T2D) and neurological diseases (NDs).
Figure 7
Figure 7
The DEGs-miRNAs interactions regulating the differentially expressed genes common between type 2 diabetes (T2D) and neurological diseases (NDs).
Figure 8
Figure 8
Disease network of type 2 diabetes (T2D) with neurological diseases (NDs) where robin’s egg blue colour round shaped nodes represent genes and octagon shaped red colour nodes represent T2D and NDs.
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