Causal co-expression method with module analysis to screen drugs with specific target.

Abstract

The considerable increase of investment in research and development by the pharmaceutical industry over the past three decades has not added the number of approved new drugs. An important issue ignored by drug discovery practice is the multi-dimensional interaction network between drugs and their targets. Thus, it is essential to view drug actions through the lens of network biology. In the current study, based on the co-expression network of transcription factors and their downstream genes, we proposed a novel approach, called causal co-expression method with module analysis, to screen drugs with specific target and fewer side effects. We presented a causal co-expression method with module analysis and it could be used in analyzing the microarray data of different drug candidates. At first, the differential wiring value (DW) was calculated to find some causal transcription factors (TFs) by combining with differential expression genes in the regulated networks. After the discovery of the causal TFs, co-expression module analysis method was applied to mine molecular pharmacology pathways around these causal TFs at molecular level. We applied our methods to two drug candidates, Argyrin A and Bortezomib, both with anti-cancer activities. We first obtained some differentially expressed transcription factors of cells treated with Argyrin A or Bortezomib. Nearly all these transcription factors are associated with the tumor suppressor protein p27kip1. Furthermore, module analysis showed that Bortezomib inhibited tumor growth not specifically by cell cycle and cell proliferation pathway, but through many basic metabolic processes which result in cell toxicity. In contrast, Argyrin A had influence on cell cycle, and was involved in DNA damage repair at the same time, showing that Argyrin A was a more suitable drug for anti-cancer treatment. Our study revealed that the causal co-expression method with module analysis was effective and can be used as a tool to evaluate drug candidates.

• Authors: Shuhao Yu, Lulu Zheng, Yixue Li, Chunyan Li, Chenchen Ma, Yang Yu, Xuan Li, Pei Hao
• Journal: Gene (Gene) Vol. 518 Issue 1 Pg. 145-51 (Apr 10 2013) ISSN: 1879-0038 [Electronic] Netherlands
• PMID: 23266800 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: Copyright © 2012 Elsevier B.V. All rights reserved.

Chemical References

• Antineoplastic Agents
• Boronic Acids
• CDKN1B protein, human
• Peptides, Cyclic
• Pyrazines
• Transcription Factors
• argyrin A
• Cyclin-Dependent Kinase Inhibitor p27
• Bortezomib

Topics

• Antineoplastic Agents (pharmacology)
• Boronic Acids (pharmacology)
• Bortezomib
• Cell Cycle (drug effects, genetics)
• Cell Proliferation (drug effects)
• Cyclin-Dependent Kinase Inhibitor p27 (genetics)
• DNA Damage (drug effects, genetics)
• Data Mining (methods)
• Drug Discovery
• Drug Evaluation, Preclinical (methods)
• Gene Regulatory Networks
• Humans
• Oligonucleotide Array Sequence Analysis (methods)
• Peptides, Cyclic (pharmacology)
• Pyrazines (pharmacology)
• Transcription Factors (genetics)