Our goal was to identify topological bottlenecks genes that are involved in the innate immune response. We utilized inferred networks derived from transcriptional data from three different sources, and combined the results to identify candidate bottleneck genes that might play more important and/or universal roles in related TLR-mediated neuroprotection and innate immune processes. The first two sources are blood and brain genomic responses from a study of neuroprotection against stroke in mice using the TLR ligands, LPS, CpGODN, or brief middle cerebral artery occlusion to precondition. The brain dataset has been previously described and the blood dataset is described for the first time here. The third source is from a large TWS119 compendium study of innate immune response in mouse macrophages. The first two datasets examine innate immune responses induced by TLRs in the context of preconditioning-induced neuroprotection against stroke. The third dataset provides an isolated view of innate immune responses induced in macrophages by the administration of TLR ligands. Our hypothesis is that network analysis of transcriptional data from several systems responding to stimulation of TLR-mediated response will allow identification of key effectors of system function, in this case innate immune processes. Our computational analysis identified bottleneck genes for each dataset analyzed and determined major functional pathways that may be affected by these genes. When comparing all three datasets, we found only six conserved bottlenecks including Ifi47, Axud1, Ppp1r15a, Tgtp, Ifit1, and Oasl2. Ifit1 was further investigated by examining its conserved network neighborhood, which had several overlapping genes in each dataset. Finally, we validated the role of Ifit1 as a functional bottleneck in macrophages by showing that blocking expression of Ifit1 using siRNA dramatically reduced expression of the predicted first-order network genes Usp18 and M61. This data demonstrates that Ifit1 exerts a regulatory influence over important downstream immune genes when stimulated by LPS, though the mechanism of its action remains unclear. Using our novel approach, network construction using transcriptional data from multiple time course studies and identification of key components using topological analysis, we define six potential key modulators of innate immunity that may also contribute to the neuroprotective response produced by preconditioning. In a previous study the effects of siRNA knock-downs of 125 regulators had been assessed on a total of 126 target genes after an initial network-based analysis of TLR stimulation in dendritic cells was performed. The study focused only on transcriptional regulators and so does not include direct validation of any of our predicted shared bottlenecks. However, we assessed the regulatory coherence of the six members of the conserved Ifit1 neighborhood that were assayed in the study: Ifit1, Ifit3, Oasl1, Rsad2, Iigp2, and Irf7. We therefore assessed the correlation of expression profiles for each gene in response to the 125 regulator knock-downs inside the neighborhood versus other genes. This analysis revealed the in-group correlation to be 0.76 while the out-of-group correlation was 0.40. This is a slight improvement over the mean correlation of the neighbor genes that are not common between the networks. The profiles of each of the neighbors is shown as Supplemental Figure S3.