Unraveling dynamic activities of autocrine pathways that control drug-response transcriptome networks.
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.
Tamada
Yoshinori
Y
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
Araki
Hiromitsu
H
Imoto
Seiya
S
Nagasaki
Masao
M
Doi
Atsushi
A
Nakanishi
Yukiko
Y
Tomiyasu
Yuki
Y
Yasuda
Kaori
K
Dunmore
Ben
B
Sanders
Deborah
D
Humphreys
Sally
S
Print
Cristin
C
Charnock-Jones
D Stephen
DS
Tashiro
Kousuke
K
Kuhara
Satoru
S
Miyano
Satoru
S
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
D000960:Hypolipidemic Agents; D047493:PPAR alpha; D011345:Fenofibrate