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Knowledge-based compact disease models identify new molecular players contributing to early-stage Alzheimer’s disease

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dc.contributor.author Mayburd, Anatoly
dc.contributor.author Baranova, Ancha
dc.date.accessioned 2015-10-29T19:47:54Z
dc.date.available 2015-10-29T19:47:54Z
dc.date.issued 2013-11-07
dc.identifier.citation Mayburd and Baranova: Knowledge-based compact disease models identify new molecular players contributing to early-stage Alzheimer’s disease. BMC Systems Biology 2013 7:121. en_US
dc.identifier.uri https://hdl.handle.net/1920/10010
dc.description.abstract Background High-throughput profiling of human tissues typically yield as results the gene lists comprised of a mix of relevant molecular entities with multiple false positives that obstruct the translation of such results into mechanistic hypotheses. From general probabilistic considerations, gene lists distilled for the mechanistically relevant components can be far more useful for subsequent experimental design or data interpretation. Results The input candidate gene lists were processed into different tiers of evidence consistency established by enrichment analysis across subsets of the same experiments and across different experiments and platforms. The cut-offs were established empirically through ontological and semantic enrichment; resultant shortened gene list was re-expanded by Ingenuity Pathway Assistant tool. The resulting sub-networks provided the basis for generating mechanistic hypotheses that were partially validated by literature search. This approach differs from previous consistency-based studies in that the cut-off on the Receiver Operating Characteristic of the true-false separation process is optimized by flexible selection of the consistency building procedure. The gene list distilled by this analytic technique and its network representation were termed Compact Disease Model (CDM). Here we present the CDM signature for the study of early-stage Alzheimer’s disease. The integrated analysis of this gene signature allowed us to identify the protein traffic vesicles as prominent players in the pathogenesis of Alzheimer’s. Considering the distances and complexity of protein trafficking in neurons, it is plausible that spontaneous protein misfolding along with a shortage of growth stimulation result in neurodegeneration. Several potentially overlapping scenarios of early-stage Alzheimer pathogenesis have been discussed, with an emphasis on the protective effects of 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. Alignment with emerging literature confirmed many predictions derived from early-stage Alzheimer’s disease’ CDM. Conclusions A flexible approach for high-throughput data analysis, the Compact Disease Model generation, allows extraction of meaningful, mechanism-centered gene sets compatible with instant translation of the results into testable hypotheses.
dc.description.sponsorship Publication of this article was funded in part by the George Mason University Libraries Open Access Publishing Fund. en_US
dc.language.iso en_US en_US
dc.publisher BioMed Central en_US
dc.rights Attribution 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by/3.0/us/ *
dc.subject Signature en_US
dc.subject Network en_US
dc.subject Knowledge-based algorithms en_US
dc.subject Alzheimer’s en_US
dc.subject Protein traffic vesicles en_US
dc.subject Affymetrix en_US
dc.subject Illumina en_US
dc.subject Antihypertensive drugs en_US
dc.title Knowledge-based compact disease models identify new molecular players contributing to early-stage Alzheimer’s disease en_US
dc.type Article en_US
dc.identifier.doi http://dx.doi.org/10.1186/1752-0509-7-121


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