Analytical Strategy to Prioritize Alzheimer's Disease Candidate Genes in Gene Regulatory Networks Using Public Expression Data

Abstract

Alzheimer's disease (AD) progressively destroys cognitive abilities in the aging population with tremendous effects on memory. Despite recent progress in understanding the underlying mechanisms, high drug attrition rates have put a question mark behind our knowledge about its etiology. Re-evaluation of past studies could help us to elucidate molecular-level details of this disease. Several methods to infer such networks exist, but most of them do not elaborate on context specificity and completeness of the generated networks, missing out on lesser-known candidates. In this study, we present a novel strategy that corroborates common mechanistic patterns across large scale AD gene expression studies and further prioritizes potential biomarker candidates. To infer gene regulatory networks (GRNs), we applied an optimized version of the BC3Net algorithm, named BC3Net10, capable of deriving robust and coherent patterns. In principle, this approach initially leverages the power of literature knowledge to extract AD specific genes for generating viable networks. Our findings suggest that AD GRNs show significant enrichment for key signaling mechanisms involved in neurotransmission. Among the prioritized genes, well-known AD genes were prominent in synaptic transmission, implicated in cognitive deficits. Moreover, less intensive studied AD candidates (STX2, HLA-F, HLA-C, RAB11FIP4, ARAP3, AP2A2, ATP2B4, ITPR2, and ATP2A3) are also involved in neurotransmission, providing new insights into the underlying mechanism. To our knowledge, this is the first study to generate knowledge-instructed GRNs that demonstrates an effective way of combining literature-based knowledge and data-driven analysis to identify lesser known candidates embedded in stable and robust functional patterns across disparate datasets.

Keywords: Alzheimer’s disease; gene regulatory networks; microarray analysis; synaptic transmission.

Fig.1

The overall strategy applied to obtain robust gene expression patterns across public Alzheimer’s disease studies. Firstly, four gene expression datasets were shortlisted from NeuroTransDB database. The selected studies underwent preprocessing and quality control. In each dataset, the intensity values were limited to the seed gene list. To enrich the seed, functional enrichment was applied where genes from the identified significant pathways from each dataset’s subnetwork (edge weight >0.5), generated using BC3Net10 approach, were included. When no additional genes were identified, subnetworks from each iteration, separately for each dataset, were merged into an aggregated network for further prioritization of the genes using genetic variant analysis.

Fig.2

Venn diagram depicting the gene overlap between the subnetworks (edge weight >0.5) of the four datasets, generated using the initial seed. The initial seed was compiled from top 500 genes retrieved by querying SCAIView for Alzheimer’s disease related genes. It is evident that there are no common genes among the four dataset’s subnetworks. Differing factors between platforms, analytical methods, tissue source, etc. could contribute to such a behavior.

Fig.3

Stratification of the nodes and edges in four aggregated networks. Each stack in the bar plot represents the fraction of nodes added in that iteration (IT) relative to the aggregated network (considered as 1). The addition of nodes remained stable across the datasets in each iteration. However, the inclusion of edges varies, which could be presumed due to newly inferred interactions from the newly included nodes in each iteration. (a) Fraction of added nodes in different iterations; (b) Fraction of added edges in different iterations.

Fig.4

Mean and variance distribution across four datasets for the added nodes and edges in each iteration. Enrichment of nodes and edges reach saturation after 7th iteration, suggesting the completeness of the generated GRNs. Relatively high number of edges (see y-axis range) show immense inter-connectivity between the genes in the GRNs. (a) Boxplot for mean and variance distribution of nodes; (b) Boxplot for mean and variance distribution of edges.

Fig.5

The landscape of p-value for the final list of significant pathways. For easy visualization, we have used 1-p value instead of p-value on Y-axis. Each line in the graph represents aggregated GRN for specified dataset (see chart legend). The listed pathways show higher significance level in consensus GRN in comparison to the individual dataset aggregated GRNs.

Fig.6

Subnetworks of the three shortlisted potential pathways (extracted from consensus network) involved in neurotransmission. Nodes in Cyan are involved in more than one pathways and the size of the nodes depends on the number of pathways involved. Triangle nodes represent the presence of a SNP. (a) Calcium signaling pathway; (b) Endocytosis pathway; (c) Synaptic vesicle cycle.