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首页> 外文期刊>Current Organic Synthesis >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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A Network-Based Bioinformatics Approach to Identify Molecular Biomarkers for Type 2 Diabetes that Are Linked to the Progression of Neurological Diseases

机译：一种基于网络的生物信息学方法，用于鉴定2型糖尿病的分子生物标志物，与神经疾病的进展相关联

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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.
机译：神经系统疾病（NDS）是渐进性疾病，其中的进展可以显著影响的范围内常见的疾病即存在的合并症。临床研究，包括流行病学和神经病理学分析，表明例2型糖尿病（T2D）具有ND的恶化进展，提示NDS和T2D之间致病链接。然而，寻找因果或诱发链接T2D和NDS仍然具有挑战性的因素。为了解决这些问题，我们开发了一种高通量的基于网络的定量使用无关的方法来确定在这两个T2D和NDS异常表达的基因，以确定一些可以支撑T2D和ND互动共享的分子途径的管道。从控制和受疾病影响的个人和在T2D患者和ND组织鉴定差异表达的基因（DEGS）我们采用了基因表达的转录组数据集时相比，未受影响的对照个体。一百97度的视角（99上调和98下调受影响的个人）是为两者共同的T2D和ND数据集进行鉴定。这些识别度的视角的功能注解揭示了显著细胞信号传导相关的分子途径的参与。然后，将重叠度的视角（即，在两者T2D和ND数据集看到）被用来提取最显著GO术语。我们进行这些结果与金基准数据库和文献检索，这确定了基因和途径先前已经被链接到的NDS或T2D，哪些是新的验证。在途径毂蛋白质鉴定使用先前没有被描述为打在这些疾病中的作用的蛋白质 - 蛋白质相互作用分析（包括DNM2，DNM1，MYH14，PACSIN2，TFRC，PDE4D，ENTPD1，PLK4，CDC20B，和CDC14A）。为了揭示度的视角，我们使用转录因子（TF）的相互作用分别分析和DEG-微RNA（miRNA）的相互作用分析，转录和转录后调节。因此，我们确定了以下转录因子作为重要的推动我们的T2D / ND共同基因的表达：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和六月。影响这些基因的表达的微RNA包括的mir-335-5p，的mir-16-5p，的mir-93-5p，的mir-17-5p，的mir-124-3p。因此，我们的转录数据分析识别NDS和T2D病理学情况可依据合并症相互作用，链接，其可以包括用于治疗性干预的潜在靶标之间新的潜在联系。总之，我们的邻居，基于基准和多层网络拓扑方法鉴定的新的生物标志物推测指示型如何2型糖尿病（T2D），这些神经系统疾病相互作用和途径，在未来，可以有针对性地进行治疗。

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