Abstract

Background:

Recent studies have suggested comorbid association between Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM) through identification of shared molecular mechanisms. However, the inference is pre-dominantly literature-based and lacks interpretation of pre-disposed genomic variants and transcriptomic measurables.

Objective:

In this study, we aim to identify shared genetic variants and dysregulated genes in AD and T2DM and explore their functional roles in the comorbidity between the diseases.

Methods:

The genetic variants for AD and T2DM were retrieved from GWAS catalog, GWAS central, dbSNP, and DisGeNet and subjected to linkage disequilibrium analysis. Next, shared variants were prioritized using RegulomeDB and Polyphen-2. Afterwards, a knowledge assembly embedding prioritized variants and their corresponding genes was created by mining relevant literature using Biological Expression Language. Finally, coherently perturbed genes from gene expression meta-analysis were mapped to the knowledge assembly to pinpoint biological entities and processes and depict a mechanistic link between AD and T2DM.

Results:

Our analysis identified four genes (i.e., ABCG1, COMT, MMP9, and SOD2) that could have dual roles in both AD and T2DM. Using cartoon representation, we have illustrated a set of causal events surrounding these genes which are associated to biological processes such as oxidative stress, insulin resistance, apoptosis and cognition.

Conclusion:

Our approach of using data as the driving force for unraveling disease etiologies eliminates literature bias and enables identification of novel entities that serve as the bridge between comorbid conditions.

中文翻译：

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知识集合的数据驱动建模以了解 2 型糖尿病和阿尔茨海默病之间的共病。

摘要

背景：

最近的研究表明，通过确定共同的分子机制，阿尔茨海默病 (AD) 和 2 型糖尿病 (T2DM) 之间存在共病关联。然而，该推论主要基于文献，缺乏对预先处理的基因组变异和转录组可测量值的解释。

客观的：

在这项研究中，我们的目标是确定 AD 和 T2DM 中的共享遗传变异和失调基因，并探索它们在疾病合并症中的功能作用。

方法：

从 GWAS 目录、GWAS central、dbSNP 和 DisGeNet 中检索 AD 和 T2DM 的遗传变异，并进行连锁不平衡分析。接下来，使用 RegulomeDB 和 Polyphen-2 对共享变体进行优先排序。之后，通过使用生物表达语言挖掘相关文献，创建了嵌入优先变体及其相应基因的知识组装。最后，来自基因表达荟萃分析的一致扰动的基因被映射到知识集合，以查明生物实体和过程，并描绘 AD 和 T2DM 之间的机械联系。

结果：

我们的分析确定了四个基因（即ABCG1、COMT、MMP9和SOD2），它们在 AD 和 T2DM 中具有双重作用。使用卡通表示，我们已经说明了围绕这些基因的一组因果事件，这些事件与氧化应激、胰岛素抵抗、细胞凋亡和认知等生物过程相关。

结论：

我们使用数据作为揭示疾病病因的驱动力的方法消除了文献偏见，并能够识别作为合并症之间桥梁的新实体。