Biological Networks and Pathway Analysis

Anatoly Mayburd, Ancha Baranova

High-throughput profiling of human tissues typically yields the gene lists composed of a variety of more or less relevant molecular entities. These lists are riddle by false positive observations that often obstruct generation of mechanistic hypothesis that may explain complex phenotype. From general probabilistic considerations, the gene lists enriched by the mechanistically relevant targets can be far more useful for subsequent experimental design or data interpretation. Using Alzheimer's disease as example, the candidate gene lists were processed into different tiers of evidence consistency established by enrichment analysis across subdatasets collected within the same experiment and across different experiments and platforms. The cutoffs were established empirically through ontological and semantic enrichment; resultant shortened gene list was reexpanded by Ingenuity Pathway Assistant tool. The resulting subnetworks provided the basis for generating mechanistic hypotheses that were partially validated by mined experimental evidence. This approach differs from previous consistency-based studies in that the cutoff on the Receiver Operating Characteristic of the true-false separation process is optimized by flexible selection of the consistency building procedure. The resultant Compact Disease Models (CDM) composed of the gene list distilled by this analytic technique and its network-based representation allowed us to highlight possible role of the protein traffic vesicles in the pathogenesis of Alzheimer's. Considering the distances and complexity of protein trafficking in neurons, it is plausible to hypothesize that spontaneous protein misfolding along with a shortage of growth stimulation may provide a shortcut to neurodegeneration. Several potentially overlapping scenarios of early-stage Alzheimer pathogenesis are discussed, with an emphasis on the protective effects of Angiotensin receptor 1 (AT-1) mediated antihypertensive response on cytoskeleton remodeling, along with neuronal activation of oncogenes, luteinizing hormone signaling and insulin-related growth regulation, forming a pleiotropic model of its early stages. Compact Disease Model generation is a flexible approach for high-throughput data analysis that allows extraction of meaningful, mechanism-centered gene sets compatible with instant translation of the results into testable hypotheses.