Weighted Protein Interaction Network Analysis of Frontotemporal Dementia

The genetic analysis
of complex disorders has undoubtedly led to
the identification of a wealth of associations between genes and specific
traits. However, moving from genetics to biochemistry one gene at
a time has, to date, rather proved inefficient and under-powered to
comprehensively explain the molecular basis of phenotypes. Here we
present a novel approach, weighted protein–protein interaction
network analysis (W-PPI-NA), to highlight key functional players within
relevant biological processes associated with a given trait. This
is exemplified in the current study by applying W-PPI-NA to frontotemporal
dementia (FTD): We first built the state of the art FTD protein network
(FTD-PN) and then analyzed both its topological and functional features.
The FTD-PN resulted from the sum of the individual interactomes built
around FTD-spectrum genes, leading to a total of 4198 nodes. Twenty
nine of 4198 nodes, called inter-interactome hubs (IIHs), represented
those interactors able to bridge over 60% of the individual interactomes.
Functional annotation analysis not only reiterated and reinforced
previous findings from single genes and gene-coexpression analyses
but also indicated a number of novel potential disease related mechanisms,
including DNA damage response, gene expression
regulation, and cell waste disposal and
potential biomarkers or therapeutic targets including EP300. These
processes and targets likely represent the functional core impacted
in FTD, reflecting the underlying genetic architecture contributing
to disease. The approach presented in this study can be applied to
other complex traits for which risk-causative genes are known as it
provides a promising tool for setting the foundations for collating
genomics and wet laboratory data in a bidirectional manner. This is
and will be critical to accelerate molecular target prioritization
and drug discovery.

复杂疾病的遗传分析无疑已成功鉴定出大量基因与特定性状之间的关联。然而，迄今为止，逐基因地将研究从遗传学推进至生物化学的策略，已被证明效率低下且效力不足，无法全面阐释表型的分子基础。在此，我们提出一种全新方法——加权蛋白质相互作用网络分析（weighted protein–protein interaction network analysis, W-PPI-NA），用于识别与特定性状相关的核心生物学过程中的关键功能作用因子。本研究以额颞叶痴呆（frontotemporal dementia, FTD）为例验证该方法的有效性：我们首先构建了当前最先进的额颞叶痴呆蛋白质互作网络（FTD-PN），随后对其拓扑结构与功能特征展开分析。FTD-PN由围绕额颞叶痴呆谱系基因构建的各独立互作组整合而成，共包含4198个节点。其中29个节点被称为跨互作组枢纽（inter-interactome hubs, IIHs），这类节点能够连接超过60%的独立互作组。功能注释分析不仅重申并强化了此前单基因及基因共表达分析的研究结论，同时还揭示了多种潜在的新型疾病相关机制，包括DNA损伤应答、基因表达调控、细胞废物清除等；此外还鉴定出包括EP300在内的潜在生物标志物或治疗靶点。上述过程与靶点或可代表额颞叶痴呆中受影响的功能核心，反映了参与疾病发生的潜在遗传结构。本研究提出的方法可推广至其他已知风险致病基因的复杂性状研究，因其可为双向整合基因组学与湿实验数据搭建研究基础，是极具前景的研究工具；该方法目前乃至未来都对加速分子靶点优先级排序与药物研发至关重要。