Brain in situ hybridization maps as a source for reverse-engineering transcriptional regulatory networks: Alzheimer's disease insights

• Networks of transcriptional regulatory relationships among genes (TRNs) are insightful.
• Microarrays (MA) have been a primary gene expression data source for inferring TRNs.
• We explore Allen Brain Atlas in situ hybridization data sets (ISH) as an alternative.
• TRN from whole brain MA had only 17% of edges of those from brain region-specific ISH.
• A consensus regulatory motif in Alzheimer's involving Ncor2, Sp3 and Usf2 emerged.

Microarray data have been a valuable resource for identifying transcriptional regulatory relationships among genes. As an example, brain region-specific transcriptional regulatory events have the potential of providing etiological insights into Alzheimer Disease (AD). However, there is often a paucity of suitable brain-region specific expression data obtained via microarrays or other high throughput means. The Allen Brain Atlas in situ hybridization (ISH) data sets (Jones et al., 2009) represent a potentially valuable alternative source of high-throughput brain region-specific gene expression data for such purposes. In this study, Allen Brain Atlas mouse ISH data in the hippocampal fields were extracted, focusing on 508 genes relevant to neurodegeneration. Transcriptional regulatory networks were learned using three high-performing network inference algorithms. Only 17% of regulatory edges from a network reverse-engineered based on brain region-specific ISH data were also found in a network constructed upon gene expression correlations in mouse whole brain microarrays, thus showing the specificity of gene expression within brain sub-regions. Furthermore, the ISH data-based networks were used to identify instructive transcriptional regulatory relationships. Ncor2, Sp3 and Usf2 form a unique three-party regulatory motif, potentially affecting memory formation pathways. Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2). Further, Nfe2l1, Egr1 and Usf2 are sensitive to dietary factors and could be among links between dietary influences and genes in the AD etiology. Thus, this approach of harnessing brain region-specific ISH data represents a rare opportunity for gleaning unique etiological insights for diseases such as AD.