title: Unraveling dynamic activities of autocrine pathways that control drug-response transcriptome networks.
abstract: Some drugs affect secretion of secreted proteins (e.g. cytokines) released from target cells, but it remains unclear whether these proteins act in an autocrine manner and directly effect the cells on which the drugs act. In this study, we propose a computational method for testing a biological hypothesis: there exist autocrine signaling pathways that are dynamically regulated by drug response transcriptome networks and control them simultaneously. If such pathways are identified, they could be useful for revealing drug mode-of-action and identifying novel drug targets. By the node-set separation method proposed, dynamic structural changes can be embedded in transcriptome networks that enable us to find master-regulator genes or critical paths at each observed time. We then combine the protein-protein interaction network with the estimated dynamic transcriptome network to discover drug-affected autocrine pathways if they exist. The statistical significance (p-values) of the pathways are evaluated by the meta-analysis technique. The dynamics of the interactions between the transcriptome networks and the signaling pathways will be shown in this framework. We illustrate our strategy by an application using anti-hyperlipidemia drug, Fenofibrate. From over one million protein-protein interaction pathways, we extracted significant 23 autocrine-like pathways with the Bonferroni correction, including VEGF-NRP1-GIPC1-PRKCA-PPARalpha, that is one of the most significant ones and contains PPARalpha, a target of Fenofibrate.
authors.affiliation: Human Genome Center, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. tamada@ims.u-tokyo.ac.jp
authors.forename: Yoshinori
authors.initials: Y
authors.lastname: Tamada
authors.forename: Hiromitsu
authors.initials: H
authors.lastname: Araki
authors.forename: Seiya
authors.initials: S
authors.lastname: Imoto
authors.forename: Masao
authors.initials: M
authors.lastname: Nagasaki
authors.forename: Atsushi
authors.initials: A
authors.lastname: Doi
authors.forename: Yukiko
authors.initials: Y
authors.lastname: Nakanishi
authors.forename: Yuki
authors.initials: Y
authors.lastname: Tomiyasu
authors.forename: Kaori
authors.initials: K
authors.lastname: Yasuda
authors.forename: Ben
authors.initials: B
authors.lastname: Dunmore
authors.forename: Deborah
authors.initials: D
authors.lastname: Sanders
authors.forename: Sally
authors.initials: S
authors.lastname: Humphreys
authors.forename: Cristin
authors.initials: C
authors.lastname: Print
authors.forename: D Stephen
authors.initials: DS
authors.lastname: Charnock-Jones
authors.forename: Kousuke
authors.initials: K
authors.lastname: Tashiro
authors.forename: Satoru
authors.initials: S
authors.lastname: Kuhara
authors.forename: Satoru
authors.initials: S
authors.lastname: Miyano
mesh_terms: D019898:Autocrine Communication / Q000187:drug effects* / Q000235:genetics*; D001499:Bayes Theorem; D001699:Biometry; D002478:Cells, Cultured; D016208:Databases, Factual; D030541:Databases, Genetic; D011345:Fenofibrate / Q000494:pharmacology; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D053263:Gene Regulatory Networks; D006801:Humans; D000960:Hypolipidemic Agents / Q000494:pharmacology; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D047493:PPAR alpha / Q000819:agonists / Q000235:genetics; D010597:Pharmacogenetics / Q000706:statistics & numerical data; D025941:Protein Interaction Mapping / Q000706:statistics & numerical data
chemical_list: D000960:Hypolipidemic Agents; D047493:PPAR alpha; D011345:Fenofibrate